Task

GlycanML taxonomy prediction

This GlycanML task evaluates taxonomy classification using glycan representations.

Sources (3)

GlycanML/GlycanML official source; GlycanML/GlycanML configs/single_task/BERT/species_BERT.yaml; GlycanML/GlycanML module/custom_datasets/glycan_classification.py · Pinned README: Introduction; Experiment configurations; leaderboard; pinned configs/single_task/BERT/species_BERT.yaml, module/custom_datasets/glycan_classification.py (task metric, dataset class and split methods)

190 evaluations · 190 results

Overview

Datasets

Hierarchical taxonomy labels span species, genus, family, order, class, phylum, kingdom and domain; the checked single-task configuration targets species.

Metrics

Accuracy and Matthews correlation coefficient in the checked species-classification configuration.

Allowed inputs

Glycan sequence or graph representation with an organism-taxonomy target.

Sources (3)

Evaluation procedure diagram

How it worksEvaluation procedure

Conceptual procedure. Task variants and protocol versions retain their separate scoring conditions.

Sources (4)

GlycanML/GlycanML official source; GlycanML/GlycanML configs/single_task/BERT/species_BERT.yaml; GlycanML/GlycanML module/custom_datasets/glycan_classification.py; glycanml primary benchmark evidence · Pinned README: Introduction; Experiment configurations; leaderboard; pinned configs/single_task/BERT/species_BERT.yaml, module/custom_datasets/glycan_classification.py (task metric, dataset class and split methods); Sections 3.1–3.3; Table 1

Source reviewed · Automated source review, 2026-09-16. All specifications and missing details

Results

Each comparison retains its reviewed evaluation scope, dataset and metric. Results are shown without a pooled ranking.

Find a protocol, dataset or metric

Evaluation scope and datasetMetric and reported setup

GlycanML Domain · Table 3, p. 8

accuracy (percent) · Higher values are better.

SugarBase taxonomy · Domain (GlycanML taxonomy prediction) · SugarBase taxonomy · Domain

Evidence origin: Author-reported evaluation.

GlycanML: A Multi-Task and Multi-Structure Benchmark for Glycan Machine Learning · Table 3, p. 8, Domain

Taxonomic levels are distinct tasks; do not combine raw accuracy across levels.
Protein–glycan scores evaluate combined glycan encoders plus ESM-1b/MLP, not an ESM-1b standalone checkpoint.
Cluster manifests and trained checkpoint hashes are not enumerated in the paper table.

Comparison details and limitations

Author-reported single-task models on the same held-out task; not a cross-paper ranking. SugarBase taxonomy: motif-frequency K-means cluster allocation 8:1:1.

Automated source review: 2026-09-17. Numerical source review does not establish independent reproduction.

Dots show point estimates. Whiskers show only explicitly defined uncertainty (standard deviation, standard error or a labelled interval); their definitions remain in Table. Unresolved uncertainty is not plotted. Differences do not establish statistical significance.

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Showing 10 of 10 matching rows.

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Tested configuration

0255075100

Reported score

Methods and evaluation design

Procedure, tasks and evaluated configurations

How it works

Evaluation methodology

Hierarchical taxonomy labels span species, genus, family, order, class, phylum, kingdom and domain; the checked single-task configuration targets species. Dataset loader retains the train/validation/test assignments supplied in the downloaded CSV; this inspection does not establish how those original assignments were constructed. Accuracy and Matthews correlation coefficient in the checked species-classification configuration. Sequence-model CNN, ResNet, LSTM and BERT configurations; graph-model GCN, RGCN, GAT, GIN, CompGCN and MPNN configurations.

Sources (3)

Evaluation design

Benchmarks bring together tasks and protocols. A task describes the biological question; a protocol defines a particular test.

Benchmarks

GlycanML

Protocols

These source-backed links do not make different protocols or scores interchangeable.

Run this benchmark

Choose a concrete protocol before running an evaluation. Its inputs, split and scoring rules determine which results can be compared.

Run instructions

No runnable recipe has been reviewed for this task. Dataset access, model requirements, licences and compute requirements must be checked against its sources before execution.

A task describes a biological question. Choose a linked protocol to obtain concrete split and scoring instructions.

Strengths, limitations and unresolved questions

Strengths and limitations

Strengths supported by sources

Shared configurations allow controlled comparisons between single-task and multi-task learning.
Sources (3)
GlycanML/GlycanML official source; GlycanML/GlycanML configs/single_task/BERT/species_BERT.yaml; GlycanML/GlycanML module/custom_datasets/glycan_classification.py · Pinned README: Introduction; Experiment configurations; leaderboard; pinned configs/single_task/BERT/species_BERT.yaml, module/custom_datasets/glycan_classification.py (task metric, dataset class and split methods)

Limitations and conditions

Taxonomy, immunogenicity and glycosylation tasks hold out glycan motif clusters; interaction prediction holds out protein clusters. These boundaries do not imply that both proteins and glycans are unseen in the interaction task.
Sources
glycanml primary benchmark evidence · Sections 3.1–3.4 and 5.1; Tables 1 and 3

Profile review details

Primary paper and/or task implementation reviewed for the explicitly cited methodology claims. Scope-limited absence is recorded only after the documented source search; no model runs or independent reproduction.

Stable record: discovery-benchmark-glycanml-taxonomy-prediction

Specifications

Inputs, training, access and other details

Explanatory profile: source reviewed · Automated source review, 2026-09-16. Review applies to the cited claims; unresolved fields are listed below. Numerical results retain their own review status.

Data, procedure and scoring
Property	Description and evidence
Datasets	Hierarchical taxonomy labels span species, genus, family, order, class, phylum, kingdom and domain; the checked single-task configuration targets species. Sources (3) GlycanML/GlycanML official source; GlycanML/GlycanML configs/single_task/BERT/species_BERT.yaml; GlycanML/GlycanML module/custom_datasets/glycan_classification.py · Pinned README: Introduction; Experiment configurations; leaderboard; pinned configs/single_task/BERT/species_BERT.yaml, module/custom_datasets/glycan_classification.py (task metric, dataset class and split methods)
Splits	Glycans are represented by motif frequencies and clustered; motif groups are allocated to training, validation and test in an 8:1:1 grouping scheme. Table 1 preserves the resulting dataset-specific counts. Sources glycanml primary benchmark evidence · Sections 3.1–3.3; Table 1
Metrics	Accuracy and Matthews correlation coefficient in the checked species-classification configuration. Sources (3) GlycanML/GlycanML official source; GlycanML/GlycanML configs/single_task/BERT/species_BERT.yaml; GlycanML/GlycanML module/custom_datasets/glycan_classification.py · Pinned README: Introduction; Experiment configurations; leaderboard; pinned configs/single_task/BERT/species_BERT.yaml, module/custom_datasets/glycan_classification.py (task metric, dataset class and split methods)
Baselines	Sequence-model CNN, ResNet, LSTM and BERT configurations; graph-model GCN, RGCN, GAT, GIN, CompGCN and MPNN configurations. Sources (3) GlycanML/GlycanML official source; GlycanML/GlycanML configs/single_task/BERT/species_BERT.yaml; GlycanML/GlycanML module/custom_datasets/glycan_classification.py · Pinned README: Introduction; Experiment configurations; leaderboard; pinned configs/single_task/BERT/species_BERT.yaml, module/custom_datasets/glycan_classification.py (task metric, dataset class and split methods)
Leakage controls	Motif-based cluster separation tests transfer to structurally different glycans. This is a glycan-structure control, not a claim that all organisms or source studies are held out. Sources glycanml primary benchmark evidence · Sections 3.1–3.3; Table 1
Uncertainty	Every experiment uses seeds 0, 1 and 2; reported summaries are the mean and standard deviation over those three runs. Sources glycanml primary benchmark evidence · Sections 3.1–3.4 and 5.1; Tables 1 and 3
Entity type	Constituent benchmark task: GlycanML taxonomy prediction Sources (3) GlycanML/GlycanML official source; GlycanML/GlycanML configs/single_task/BERT/species_BERT.yaml; GlycanML/GlycanML module/custom_datasets/glycan_classification.py · Pinned README: Introduction; Experiment configurations; leaderboard; pinned configs/single_task/BERT/species_BERT.yaml, module/custom_datasets/glycan_classification.py (task metric, dataset class and split methods)
Organisms	Taxonomy tasks explicitly predict organism categories; species scope depends on the constituent dataset. Sources (3) GlycanML/GlycanML official source; GlycanML/GlycanML configs/single_task/BERT/species_BERT.yaml; GlycanML/GlycanML module/custom_datasets/glycan_classification.py · Pinned README: Introduction; Experiment configurations; leaderboard; pinned configs/single_task/BERT/species_BERT.yaml, module/custom_datasets/glycan_classification.py (task metric, dataset class and split methods)
Assays	Taxonomy, immunogenicity, glycosylation-type and protein–glycan interaction annotations. Sources (3) GlycanML/GlycanML official source; GlycanML/GlycanML configs/single_task/BERT/species_BERT.yaml; GlycanML/GlycanML module/custom_datasets/glycan_classification.py · Pinned README: Introduction; Experiment configurations; leaderboard; pinned configs/single_task/BERT/species_BERT.yaml, module/custom_datasets/glycan_classification.py (task metric, dataset class and split methods)
Allowed inputs	Glycan sequence or graph representation with an organism-taxonomy target. Sources (3) GlycanML/GlycanML official source; GlycanML/GlycanML configs/single_task/BERT/species_BERT.yaml; GlycanML/GlycanML module/custom_datasets/glycan_classification.py · Pinned README: Introduction; Experiment configurations; leaderboard; pinned configs/single_task/BERT/species_BERT.yaml, module/custom_datasets/glycan_classification.py (task metric, dataset class and split methods)
Adaptation	Separate single-task and multi-task training configurations are supplied. Sources (3) GlycanML/GlycanML official source; GlycanML/GlycanML configs/single_task/BERT/species_BERT.yaml; GlycanML/GlycanML module/custom_datasets/glycan_classification.py · Pinned README: Introduction; Experiment configurations; leaderboard; pinned configs/single_task/BERT/species_BERT.yaml, module/custom_datasets/glycan_classification.py (task metric, dataset class and split methods)

Evidence

Source checking verifies the cited claim or transcription. It does not establish independent reproduction.

Papers and result coverage

Last literature check: 2026-09-17. Primary-paper within-study comparison; numerical results not independently reproduced.

Paper or primary resource	Version	Reference
GlycanML: A Multi-Task and Multi-Structure Benchmark for Glycan Machine Learning	arXiv:2405.16206v1, 2024-05-25	Read source

Historical gaps recorded on 2026-09-17

The catalogue now holds 190 result rows for this benchmark. A note below about pending extraction describes the state on 2026-09-17 and may since have been answered by a later batch. The result rows and their sources are the current record.

Paper table does not enumerate trained checkpoint hashes; source configuration names retained.

Search and extraction details

complete comparison extracted

Searches

GlycanML benchmark 2405.16206

Evidence locations

Sections 3–5
Tables 1–4

Evidence table

Inspect claims, sources and review details

Trace each statement to its source and review. A context-only reference supports the record generally; it does not verify an individual field. Source checking does not reproduce an experiment.

One row per statement and cited source. Multiple citations are not independent evaluations. Shared locators are labelled explicitly.

50 evidence rows matching the loaded filters

Claims, original sources and review scope · Release 2026-09-29-06401fd5b220
Property and statement	Original source and location	Review and provenance
Diagram caption Conceptual procedure. Task variants and protocol versions retain their separate scoring conditions. Individual claims	GlycanML/GlycanML module/custom_datasets/glycan_classification.py Original source ↗ Pinned README: Introduction; Experiment configurations; leaderboard; pinned configs/single_task/BERT/species_BERT.yaml, module/custom_datasets/glycan_classification.py (task metric, dataset class and split methods); Sections 3.1–3.3; Table 1 Shared locator for this statement’s cited sources; not a separate locator for each citation. Version: 9f392aa6f9c6d74a296a250199beb347923d04e0 Retrieved: 2026-09-16T20:43:24.344407+00:00	source checked automated source review · 2026-09-16 Audit details Primary paper and/or task implementation reviewed for the explicitly cited methodology claims. Scope-limited absence is recorded only after the documented source search; no model runs or independent reproduction. Field: `attributes.profile.diagram.caption` Source artifact SHA-256: `1a385c4afcbfc3d2f1f319cf03fc0f4be5a1e9848cd51f9df4239cbd39771530` Hash scope: Hash scope not separately documented; inspect source record Inspected artifact
Diagram caption Conceptual procedure. Task variants and protocol versions retain their separate scoring conditions. Individual claims	GlycanML/GlycanML configs/single_task/BERT/species_BERT.yaml Original source ↗ Pinned README: Introduction; Experiment configurations; leaderboard; pinned configs/single_task/BERT/species_BERT.yaml, module/custom_datasets/glycan_classification.py (task metric, dataset class and split methods); Sections 3.1–3.3; Table 1 Shared locator for this statement’s cited sources; not a separate locator for each citation. Version: 9f392aa6f9c6d74a296a250199beb347923d04e0 Retrieved: 2026-09-16T20:42:46.675170+00:00	source checked automated source review · 2026-09-16 Audit details Primary paper and/or task implementation reviewed for the explicitly cited methodology claims. Scope-limited absence is recorded only after the documented source search; no model runs or independent reproduction. Field: `attributes.profile.diagram.caption` Source artifact SHA-256: `474995bb150c76479b0c99b6f0b2de49f5b6ad626eae1751e2f53774a4fbf38e` Hash scope: Hash scope not separately documented; inspect source record Inspected artifact
Diagram caption Conceptual procedure. Task variants and protocol versions retain their separate scoring conditions. Individual claims	glycanml primary benchmark evidence Original source ↗ Pinned README: Introduction; Experiment configurations; leaderboard; pinned configs/single_task/BERT/species_BERT.yaml, module/custom_datasets/glycan_classification.py (task metric, dataset class and split methods); Sections 3.1–3.3; Table 1 Shared locator for this statement’s cited sources; not a separate locator for each citation. Version: 2405.16206v1 Retrieved: 2026-09-16T21:04:56.017006+00:00	source checked automated source review · 2026-09-16 Audit details Primary paper and/or task implementation reviewed for the explicitly cited methodology claims. Scope-limited absence is recorded only after the documented source search; no model runs or independent reproduction. Field: `attributes.profile.diagram.caption` Source artifact SHA-256: `9ba3db678b4550898a612b42e8832bf9dee40935090fc4d969ba8f6ac5106979` Hash scope: Hash scope not separately documented; inspect source record Inspected artifact
Diagram caption Conceptual procedure. Task variants and protocol versions retain their separate scoring conditions. Individual claims	GlycanML/GlycanML official source Original source ↗ Pinned README: Introduction; Experiment configurations; leaderboard; pinned configs/single_task/BERT/species_BERT.yaml, module/custom_datasets/glycan_classification.py (task metric, dataset class and split methods); Sections 3.1–3.3; Table 1 Shared locator for this statement’s cited sources; not a separate locator for each citation. Version: 9f392aa6f9c6d74a296a250199beb347923d04e0 Retrieved: 2026-09-16T10:30:24.094166+00:00	source checked automated source review · 2026-09-16 Audit details Primary paper and/or task implementation reviewed for the explicitly cited methodology claims. Scope-limited absence is recorded only after the documented source search; no model runs or independent reproduction. Field: `attributes.profile.diagram.caption` Source artifact SHA-256: `5237cd1af3f1d7b9cf07cfe3cf722987bb27e10ee356ec108584943446bbb836` Hash scope: Hash scope not separately documented; inspect source record
Diagram steps Allowed inputs: Glycan sequence or graph representation with an organism-taxonomy target. Splits: Glycans are represented by motif frequencies and clustered; motif groups are allocated to training, validation and test in an 8:1:1 grouping scheme. Table 1 preserves the resulting dataset-specific counts. Metrics: Accuracy and Matthews correlation coefficient in the checked species-classification configuration. Individual claims	GlycanML/GlycanML module/custom_datasets/glycan_classification.py Original source ↗ Pinned README: Introduction; Experiment configurations; leaderboard; pinned configs/single_task/BERT/species_BERT.yaml, module/custom_datasets/glycan_classification.py (task metric, dataset class and split methods); Sections 3.1–3.3; Table 1 Shared locator for this statement’s cited sources; not a separate locator for each citation. Version: 9f392aa6f9c6d74a296a250199beb347923d04e0 Retrieved: 2026-09-16T20:43:24.344407+00:00	source checked automated source review · 2026-09-16 Audit details Primary paper and/or task implementation reviewed for the explicitly cited methodology claims. Scope-limited absence is recorded only after the documented source search; no model runs or independent reproduction. Field: `attributes.profile.diagram.steps` Source artifact SHA-256: `1a385c4afcbfc3d2f1f319cf03fc0f4be5a1e9848cd51f9df4239cbd39771530` Hash scope: Hash scope not separately documented; inspect source record Inspected artifact
Diagram steps Allowed inputs: Glycan sequence or graph representation with an organism-taxonomy target. Splits: Glycans are represented by motif frequencies and clustered; motif groups are allocated to training, validation and test in an 8:1:1 grouping scheme. Table 1 preserves the resulting dataset-specific counts. Metrics: Accuracy and Matthews correlation coefficient in the checked species-classification configuration. Individual claims	GlycanML/GlycanML configs/single_task/BERT/species_BERT.yaml Original source ↗ Pinned README: Introduction; Experiment configurations; leaderboard; pinned configs/single_task/BERT/species_BERT.yaml, module/custom_datasets/glycan_classification.py (task metric, dataset class and split methods); Sections 3.1–3.3; Table 1 Shared locator for this statement’s cited sources; not a separate locator for each citation. Version: 9f392aa6f9c6d74a296a250199beb347923d04e0 Retrieved: 2026-09-16T20:42:46.675170+00:00	source checked automated source review · 2026-09-16 Audit details Primary paper and/or task implementation reviewed for the explicitly cited methodology claims. Scope-limited absence is recorded only after the documented source search; no model runs or independent reproduction. Field: `attributes.profile.diagram.steps` Source artifact SHA-256: `474995bb150c76479b0c99b6f0b2de49f5b6ad626eae1751e2f53774a4fbf38e` Hash scope: Hash scope not separately documented; inspect source record Inspected artifact
Diagram steps Allowed inputs: Glycan sequence or graph representation with an organism-taxonomy target. Splits: Glycans are represented by motif frequencies and clustered; motif groups are allocated to training, validation and test in an 8:1:1 grouping scheme. Table 1 preserves the resulting dataset-specific counts. Metrics: Accuracy and Matthews correlation coefficient in the checked species-classification configuration. Individual claims	glycanml primary benchmark evidence Original source ↗ Pinned README: Introduction; Experiment configurations; leaderboard; pinned configs/single_task/BERT/species_BERT.yaml, module/custom_datasets/glycan_classification.py (task metric, dataset class and split methods); Sections 3.1–3.3; Table 1 Shared locator for this statement’s cited sources; not a separate locator for each citation. Version: 2405.16206v1 Retrieved: 2026-09-16T21:04:56.017006+00:00	source checked automated source review · 2026-09-16 Audit details Primary paper and/or task implementation reviewed for the explicitly cited methodology claims. Scope-limited absence is recorded only after the documented source search; no model runs or independent reproduction. Field: `attributes.profile.diagram.steps` Source artifact SHA-256: `9ba3db678b4550898a612b42e8832bf9dee40935090fc4d969ba8f6ac5106979` Hash scope: Hash scope not separately documented; inspect source record Inspected artifact
Diagram steps Allowed inputs: Glycan sequence or graph representation with an organism-taxonomy target. Splits: Glycans are represented by motif frequencies and clustered; motif groups are allocated to training, validation and test in an 8:1:1 grouping scheme. Table 1 preserves the resulting dataset-specific counts. Metrics: Accuracy and Matthews correlation coefficient in the checked species-classification configuration. Individual claims	GlycanML/GlycanML official source Original source ↗ Pinned README: Introduction; Experiment configurations; leaderboard; pinned configs/single_task/BERT/species_BERT.yaml, module/custom_datasets/glycan_classification.py (task metric, dataset class and split methods); Sections 3.1–3.3; Table 1 Shared locator for this statement’s cited sources; not a separate locator for each citation. Version: 9f392aa6f9c6d74a296a250199beb347923d04e0 Retrieved: 2026-09-16T10:30:24.094166+00:00	source checked automated source review · 2026-09-16 Audit details Primary paper and/or task implementation reviewed for the explicitly cited methodology claims. Scope-limited absence is recorded only after the documented source search; no model runs or independent reproduction. Field: `attributes.profile.diagram.steps` Source artifact SHA-256: `5237cd1af3f1d7b9cf07cfe3cf722987bb27e10ee356ec108584943446bbb836` Hash scope: Hash scope not separately documented; inspect source record
Diagram title Evaluation procedure Individual claims	GlycanML/GlycanML module/custom_datasets/glycan_classification.py Original source ↗ Pinned README: Introduction; Experiment configurations; leaderboard; pinned configs/single_task/BERT/species_BERT.yaml, module/custom_datasets/glycan_classification.py (task metric, dataset class and split methods); Sections 3.1–3.3; Table 1 Shared locator for this statement’s cited sources; not a separate locator for each citation. Version: 9f392aa6f9c6d74a296a250199beb347923d04e0 Retrieved: 2026-09-16T20:43:24.344407+00:00	source checked automated source review · 2026-09-16 Audit details Primary paper and/or task implementation reviewed for the explicitly cited methodology claims. Scope-limited absence is recorded only after the documented source search; no model runs or independent reproduction. Field: `attributes.profile.diagram.title` Source artifact SHA-256: `1a385c4afcbfc3d2f1f319cf03fc0f4be5a1e9848cd51f9df4239cbd39771530` Hash scope: Hash scope not separately documented; inspect source record Inspected artifact
Diagram title Evaluation procedure Individual claims	GlycanML/GlycanML configs/single_task/BERT/species_BERT.yaml Original source ↗ Pinned README: Introduction; Experiment configurations; leaderboard; pinned configs/single_task/BERT/species_BERT.yaml, module/custom_datasets/glycan_classification.py (task metric, dataset class and split methods); Sections 3.1–3.3; Table 1 Shared locator for this statement’s cited sources; not a separate locator for each citation. Version: 9f392aa6f9c6d74a296a250199beb347923d04e0 Retrieved: 2026-09-16T20:42:46.675170+00:00	source checked automated source review · 2026-09-16 Audit details Primary paper and/or task implementation reviewed for the explicitly cited methodology claims. Scope-limited absence is recorded only after the documented source search; no model runs or independent reproduction. Field: `attributes.profile.diagram.title` Source artifact SHA-256: `474995bb150c76479b0c99b6f0b2de49f5b6ad626eae1751e2f53774a4fbf38e` Hash scope: Hash scope not separately documented; inspect source record Inspected artifact

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Release 2026-09-29-06401fd5b220 · Record review: discovered

5 source records and release history

GlycanML/GlycanML official source · Original source · 9f392aa6f9c6d74a296a250199beb347923d04e0
GlycanML: A Multi-Task and Multi-Structure Benchmark for Glycan Machine Learning · Original source · arXiv:2405.16206v1, 2024-05-25
GlycanML/GlycanML configs/single_task/BERT/species_BERT.yaml · Original source · 9f392aa6f9c6d74a296a250199beb347923d04e0
GlycanML/GlycanML module/custom_datasets/glycan_classification.py · Original source · 9f392aa6f9c6d74a296a250199beb347923d04e0
glycanml primary benchmark evidence · Original source · 2405.16206v1

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Technical metadata and extraction receipts

Stable ID: discovery-benchmark-glycanml-taxonomy-prediction

areas: glycomics
entity level: task
scope note: Specialist molecular or omics evaluation; protocol details require review before numerical comparison.
task: taxonomy prediction
version: Not reported
comparison panels: id: glycanml-v1-single-domain; title: GlycanML Domain · Table 3, p. 8; protocol id: paper-protocol-b51570e2bf69ba6542; dataset id: paper-dataset-ce710f303c24cb91c2; metric: accuracy; unit: percent; direction: higher; result ids: paper-result-075ca33548bf92ef7e; paper-result-0b5a6c78c01691f3ee; paper-result-d0bee0d884f958e49b; paper-result-4543142c4187cbea18; paper-result-ea2bf935a59e8b5693; paper-result-a4c79e5939d76f3983; paper-result-c3d21b293a8aa00c67; paper-result-5c30f19e8d1c4b91ca; paper-result-2aed0631283783ea66; paper-result-22b89e6bc2ccf2fe1b; source ids: expansion-p3-glycanml-2405-16206v1; source locator: Table 3, p. 8, Domain; context: Author-reported single-task models on the same held-out task; not a cross-paper ranking. SugarBase taxonomy: motif-frequency K-means cluster allocation 8:1:1.; caveats: Taxonomic levels are distinct tasks; do not combine raw accuracy across levels.; Protein–glycan scores evaluate combined glycan encoders plus ESM-1b/MLP, not an ESM-1b standalone checkpoint.; Cluster manifests and trained checkpoint hashes are not enumerated in the paper table.; review: method: automated_source_review; date: 2026-09-17; id: glycanml-v1-single-kingdom; title: GlycanML Kingdom · Table 3, p. 8; protocol id: paper-protocol-468fdd0f4237c041d5; dataset id: paper-dataset-d7ac4e0285fbaefcab; metric: accuracy; unit: percent; direction: higher; result ids: paper-result-09a8ee00d327d0e580; paper-result-f088c48c3867898d15; paper-result-362d36486fd109e109; paper-result-a50f7f1f26ebaa5d42; paper-result-45611bc39c803891d0; paper-result-f99f24cc69642bce07; paper-result-a0c917c8b7a0bf0388; paper-result-410bae2881489d32dd; paper-result-8dea7f67a6307f6e1f; paper-result-98203c7be8a19baf6c; source ids: expansion-p3-glycanml-2405-16206v1; source locator: Table 3, p. 8, Kingdom; context: Author-reported single-task models on the same held-out task; not a cross-paper ranking. SugarBase taxonomy: motif-frequency K-means cluster allocation 8:1:1.; caveats: Taxonomic levels are distinct tasks; do not combine raw accuracy across levels.; Protein–glycan scores evaluate combined glycan encoders plus ESM-1b/MLP, not an ESM-1b standalone checkpoint.; Cluster manifests and trained checkpoint hashes are not enumerated in the paper table.; review: method: automated_source_review; date: 2026-09-17; id: glycanml-v1-single-phylum; title: GlycanML Phylum · Table 3, p. 8; protocol id: paper-protocol-a67c6f1eaf73494953; dataset id: paper-dataset-13c71587fb39174bb1; metric: accuracy; unit: percent; direction: higher; result ids: paper-result-86a3427fdae5f397c2; paper-result-c861ec21227123bf0b; paper-result-c66956e19c73fa9abf; paper-result-c1f20eec6e0881c783; paper-result-82156f189d0b3f8cd9; paper-result-3e5677e0c95596dcd7; paper-result-34a6e2a047a75ce39d; paper-result-2dfea46ca6f79b2ec1; paper-result-778ad7ec5934e369ed; paper-result-c99e02301c02669cc1; source ids: expansion-p3-glycanml-2405-16206v1; source locator: Table 3, p. 8, Phylum; context: Author-reported single-task models on the same held-out task; not a cross-paper ranking. SugarBase taxonomy: motif-frequency K-means cluster allocation 8:1:1.; caveats: Taxonomic levels are distinct tasks; do not combine raw accuracy across levels.; Protein–glycan scores evaluate combined glycan encoders plus ESM-1b/MLP, not an ESM-1b standalone checkpoint.; Cluster manifests and trained checkpoint hashes are not enumerated in the paper table.; review: method: automated_source_review; date: 2026-09-17; id: glycanml-v1-single-class; title: GlycanML Class · Table 3, p. 8; protocol id: paper-protocol-a8799c4d8546b3ff18; dataset id: paper-dataset-f9b7538618a3e1ef5b; metric: accuracy; unit: percent; direction: higher; result ids: paper-result-32ae4afd9a4c6cc89b; paper-result-da7afa1436a5e1fae4; paper-result-504992911a85e182cc; paper-result-c459dd0862f1e48ee5; paper-result-2b302ea797608740bf; paper-result-966ff1085af74c7b89; paper-result-524b419436ec9d8192; paper-result-d9c96e1c04f27b0d48; paper-result-ed1c70949ef07f3093; paper-result-233bc2d9c1d6575a1e; source ids: expansion-p3-glycanml-2405-16206v1; source locator: Table 3, p. 8, Class; context: Author-reported single-task models on the same held-out task; not a cross-paper ranking. SugarBase taxonomy: motif-frequency K-means cluster allocation 8:1:1.; caveats: Taxonomic levels are distinct tasks; do not combine raw accuracy across levels.; Protein–glycan scores evaluate combined glycan encoders plus ESM-1b/MLP, not an ESM-1b standalone checkpoint.; Cluster manifests and trained checkpoint hashes are not enumerated in the paper table.; review: method: automated_source_review; date: 2026-09-17; id: glycanml-v1-single-order; title: GlycanML Order · Table 3, p. 8; protocol id: paper-protocol-fb9190db65a5f72e10; dataset id: paper-dataset-4298ac38e85e6f8048; metric: accuracy; unit: percent; direction: higher; result ids: paper-result-abee54829f2ea08080; paper-result-7281a16570ce97b5cb; paper-result-cefa58a1f691f86ec4; paper-result-35cbadb92d47ea3c60; paper-result-3f3f4af93e3f57eacd; paper-result-0d4e92bac0d910c607; paper-result-3a9fc20851e90815ea; paper-result-d8d2084714e258967b; paper-result-ac7e4ed00d0e9515da; paper-result-af527f9d744c668717; source ids: expansion-p3-glycanml-2405-16206v1; source locator: Table 3, p. 8, Order; context: Author-reported single-task models on the same held-out task; not a cross-paper ranking. SugarBase taxonomy: motif-frequency K-means cluster allocation 8:1:1.; caveats: Taxonomic levels are distinct tasks; do not combine raw accuracy across levels.; Protein–glycan scores evaluate combined glycan encoders plus ESM-1b/MLP, not an ESM-1b standalone checkpoint.; Cluster manifests and trained checkpoint hashes are not enumerated in the paper table.; review: method: automated_source_review; date: 2026-09-17; id: glycanml-v1-single-family; title: GlycanML Family · Table 3, p. 8; protocol id: paper-protocol-8dca9eabea26dee766; dataset id: paper-dataset-8d5d79e4a6a070bfff; metric: accuracy; unit: percent; direction: higher; result ids: paper-result-09e347c7674f561706; paper-result-8bbf97e6e23787da99; paper-result-f5c63db202d546dd8a; paper-result-941024aaffa398c454; paper-result-32c123a2e27ebbe79e; paper-result-4a140ae1cf5519925c; paper-result-76cbc3d2466076b944; paper-result-42fcbda81a387d226d; paper-result-e4ad7f8d96df00ea4b; paper-result-1337bae859236e6dcf; source ids: expansion-p3-glycanml-2405-16206v1; source locator: Table 3, p. 8, Family; context: Author-reported single-task models on the same held-out task; not a cross-paper ranking. SugarBase taxonomy: motif-frequency K-means cluster allocation 8:1:1.; caveats: Taxonomic levels are distinct tasks; do not combine raw accuracy across levels.; Protein–glycan scores evaluate combined glycan encoders plus ESM-1b/MLP, not an ESM-1b standalone checkpoint.; Cluster manifests and trained checkpoint hashes are not enumerated in the paper table.; review: method: automated_source_review; date: 2026-09-17; id: glycanml-v1-single-genus; title: GlycanML Genus · Table 3, p. 8; protocol id: paper-protocol-d44b40251c33ecbea1; dataset id: paper-dataset-df6da272a0a4d8de8f; metric: accuracy; unit: percent; direction: higher; result ids: paper-result-ca073eb7b8ec09d81b; paper-result-ec1560d4f26c854db8; paper-result-012e11422e843b97f7; paper-result-6d2f9510b6bb3023ca; paper-result-6a9cd20bbb10eaeb7e; paper-result-5e1004788b7b7c0281; paper-result-f3d9c9bc60ec3b9cc7; paper-result-e30b9280e6a5a6fd1c; paper-result-fe8185d4fa2fa84e4e; paper-result-fae731c13691351a52; source ids: expansion-p3-glycanml-2405-16206v1; source locator: Table 3, p. 8, Genus; context: Author-reported single-task models on the same held-out task; not a cross-paper ranking. SugarBase taxonomy: motif-frequency K-means cluster allocation 8:1:1.; caveats: Taxonomic levels are distinct tasks; do not combine raw accuracy across levels.; Protein–glycan scores evaluate combined glycan encoders plus ESM-1b/MLP, not an ESM-1b standalone checkpoint.; Cluster manifests and trained checkpoint hashes are not enumerated in the paper table.; review: method: automated_source_review; date: 2026-09-17; id: glycanml-v1-single-species; title: GlycanML Species · Table 3, p. 8; protocol id: paper-protocol-0d66c7cabff9d799d5; dataset id: paper-dataset-4c48251022ccb53313; metric: accuracy; unit: percent; direction: higher; result ids: paper-result-dc5501dc1954b54a36; paper-result-825d6b6597e463e7cc; paper-result-fedc9b0d7fadb8f74e; paper-result-19f2505d003bc76dcc; paper-result-e9a6b117f8a49863be; paper-result-33876cb9abd2083b08; paper-result-fad4c5f6d2c179bb3f; paper-result-77f9c63846886893da; paper-result-723b93ccacba688198; paper-result-e74c30428cded73273; source ids: expansion-p3-glycanml-2405-16206v1; source locator: Table 3, p. 8, Species; context: Author-reported single-task models on the same held-out task; not a cross-paper ranking. SugarBase taxonomy: motif-frequency K-means cluster allocation 8:1:1.; caveats: Taxonomic levels are distinct tasks; do not combine raw accuracy across levels.; Protein–glycan scores evaluate combined glycan encoders plus ESM-1b/MLP, not an ESM-1b standalone checkpoint.; Cluster manifests and trained checkpoint hashes are not enumerated in the paper table.; review: method: automated_source_review; date: 2026-09-17; id: glycanml-v1-mtl-0-domain; title: GlycanML Domain · Table 4, p. 9; protocol id: paper-protocol-b51570e2bf69ba6542; dataset id: paper-dataset-ce710f303c24cb91c2; metric: accuracy; unit: percent; direction: higher; result ids: paper-result-075ca33548bf92ef7e; paper-result-fdb107a7ebf70f41b9; paper-result-0f87601e912c4389c4; paper-result-cea1fd4ede7da082f0; paper-result-a1d4fa5ae3eeb76bef; paper-result-425410a32e12403336; paper-result-7d6bfd8ae273536a43; source ids: expansion-p3-glycanml-2405-16206v1; source locator: Table 4, p. 9, Domain; context: Fixed backbone; compare adaptation strategies, not standalone foundation models. SugarBase taxonomy: motif-frequency K-means cluster allocation 8:1:1.; caveats: Mean accuracy is an unweighted aggregate of eight taxonomic tasks as reported; keep separate from every individual level.; The six MTL approaches change training, so do not label their scores as the base encoder alone.; review: method: automated_source_review; date: 2026-09-17; id: glycanml-v1-mtl-0-kingdom; title: GlycanML Kingdom · Table 4, p. 9; protocol id: paper-protocol-468fdd0f4237c041d5; dataset id: paper-dataset-d7ac4e0285fbaefcab; metric: accuracy; unit: percent; direction: higher; result ids: paper-result-09a8ee00d327d0e580; paper-result-b8f593d66e81d8176a; paper-result-8ac1e91e821535aaff; paper-result-1c68188144c78ba223; paper-result-59aaa70a642c016c71; paper-result-3a2676e4651199a4c7; paper-result-5f6263a893e28a7406; source ids: expansion-p3-glycanml-2405-16206v1; source locator: Table 4, p. 9, Kingdom; context: Fixed backbone; compare adaptation strategies, not standalone foundation models. SugarBase taxonomy: motif-frequency K-means cluster allocation 8:1:1.; caveats: Mean accuracy is an unweighted aggregate of eight taxonomic tasks as reported; keep separate from every individual level.; The six MTL approaches change training, so do not label their scores as the base encoder alone.; review: method: automated_source_review; date: 2026-09-17; id: glycanml-v1-mtl-0-phylum; title: GlycanML Phylum · Table 4, p. 9; protocol id: paper-protocol-a67c6f1eaf73494953; dataset id: paper-dataset-13c71587fb39174bb1; metric: accuracy; unit: percent; direction: higher; result ids: paper-result-86a3427fdae5f397c2; paper-result-8c1657f118a2dd808c; paper-result-ad0d1dd5c3cfc326af; paper-result-9dc690711bc090f19a; paper-result-a0214b935eba01b7a7; paper-result-43fa9c20d6d6b41325; paper-result-aec7ac1f09709fe2e2; source ids: expansion-p3-glycanml-2405-16206v1; source locator: Table 4, p. 9, Phylum; context: Fixed backbone; compare adaptation strategies, not standalone foundation models. SugarBase taxonomy: motif-frequency K-means cluster allocation 8:1:1.; caveats: Mean accuracy is an unweighted aggregate of eight taxonomic tasks as reported; keep separate from every individual level.; The six MTL approaches change training, so do not label their scores as the base encoder alone.; review: method: automated_source_review; date: 2026-09-17; id: glycanml-v1-mtl-0-class; title: GlycanML Class · Table 4, p. 9; protocol id: paper-protocol-a8799c4d8546b3ff18; dataset id: paper-dataset-f9b7538618a3e1ef5b; metric: accuracy; unit: percent; direction: higher; result ids: paper-result-32ae4afd9a4c6cc89b; paper-result-0bf3b2d166c172f494; paper-result-5cf336e78e8b9646f7; paper-result-3aa13c82aac4f30398; paper-result-750f1db97156302340; paper-result-50d6f0ba85fc1e572d; paper-result-5e2c55226c432a927b; source ids: expansion-p3-glycanml-2405-16206v1; source locator: Table 4, p. 9, Class; context: Fixed backbone; compare adaptation strategies, not standalone foundation models. SugarBase taxonomy: motif-frequency K-means cluster allocation 8:1:1.; caveats: Mean accuracy is an unweighted aggregate of eight taxonomic tasks as reported; keep separate from every individual level.; The six MTL approaches change training, so do not label their scores as the base encoder alone.; review: method: automated_source_review; date: 2026-09-17; id: glycanml-v1-mtl-0-order; title: GlycanML Order · Table 4, p. 9; protocol id: paper-protocol-fb9190db65a5f72e10; dataset id: paper-dataset-4298ac38e85e6f8048; metric: accuracy; unit: percent; direction: higher; result ids: paper-result-abee54829f2ea08080; paper-result-a299bb2064e93cd095; paper-result-56157f637f3dc28dde; paper-result-d72371bae7104d40bb; paper-result-6692b5f2ab128e869f; paper-result-b20abedfaf179beb0f; paper-result-b1a73f13e672f9f010; source ids: expansion-p3-glycanml-2405-16206v1; source locator: Table 4, p. 9, Order; context: Fixed backbone; compare adaptation strategies, not standalone foundation models. SugarBase taxonomy: motif-frequency K-means cluster allocation 8:1:1.; caveats: Mean accuracy is an unweighted aggregate of eight taxonomic tasks as reported; keep separate from every individual level.; The six MTL approaches change training, so do not label their scores as the base encoder alone.; review: method: automated_source_review; date: 2026-09-17; id: glycanml-v1-mtl-0-family; title: GlycanML Family · Table 4, p. 9; protocol id: paper-protocol-8dca9eabea26dee766; dataset id: paper-dataset-8d5d79e4a6a070bfff; metric: accuracy; unit: percent; direction: higher; result ids: paper-result-09e347c7674f561706; paper-result-89df39b51fc8f73a18; paper-result-3710d0e01f022a8f58; paper-result-76b9f005da338ff5f4; paper-result-97cf3af868ea202c20; paper-result-0f26b2626d93d81e1a; paper-result-45cd9dbf505c6c3edf; source ids: expansion-p3-glycanml-2405-16206v1; source locator: Table 4, p. 9, Family; context: Fixed backbone; compare adaptation strategies, not standalone foundation models. SugarBase taxonomy: motif-frequency K-means cluster allocation 8:1:1.; caveats: Mean accuracy is an unweighted aggregate of eight taxonomic tasks as reported; keep separate from every individual level.; The six MTL approaches change training, so do not label their scores as the base encoder alone.; review: method: automated_source_review; date: 2026-09-17; id: glycanml-v1-mtl-0-genus; title: GlycanML Genus · Table 4, p. 9; protocol id: paper-protocol-d44b40251c33ecbea1; dataset id: paper-dataset-df6da272a0a4d8de8f; metric: accuracy; unit: percent; direction: higher; result ids: paper-result-ca073eb7b8ec09d81b; paper-result-658a0bde785ae82791; paper-result-ba1188eafda83f2dff; paper-result-8ef3992de76e028698; paper-result-72ccde0fe45e939979; paper-result-4dc4b5b8e2b2f51f0d; paper-result-e2393930743e0fe2a3; source ids: expansion-p3-glycanml-2405-16206v1; source locator: Table 4, p. 9, Genus; context: Fixed backbone; compare adaptation strategies, not standalone foundation models. SugarBase taxonomy: motif-frequency K-means cluster allocation 8:1:1.; caveats: Mean accuracy is an unweighted aggregate of eight taxonomic tasks as reported; keep separate from every individual level.; The six MTL approaches change training, so do not label their scores as the base encoder alone.; review: method: automated_source_review; date: 2026-09-17; id: glycanml-v1-mtl-0-species; title: GlycanML Species · Table 4, p. 9; protocol id: paper-protocol-0d66c7cabff9d799d5; dataset id: paper-dataset-4c48251022ccb53313; metric: accuracy; unit: percent; direction: higher; result ids: paper-result-dc5501dc1954b54a36; paper-result-3decc92d3c4b47daaa; paper-result-efe8061d8b717b3d06; paper-result-994aa15ef63ab963bd; paper-result-af7e44b66703958045; paper-result-7741da71a6bffbfe05; paper-result-2a425531358cee050f; source ids: expansion-p3-glycanml-2405-16206v1; source locator: Table 4, p. 9, Species; context: Fixed backbone; compare adaptation strategies, not standalone foundation models. SugarBase taxonomy: motif-frequency K-means cluster allocation 8:1:1.; caveats: Mean accuracy is an unweighted aggregate of eight taxonomic tasks as reported; keep separate from every individual level.; The six MTL approaches change training, so do not label their scores as the base encoder alone.; review: method: automated_source_review; date: 2026-09-17; id: glycanml-v1-mtl-0-mean-acc; title: GlycanML Mean Acc (%) · Table 4, p. 9; protocol id: paper-protocol-f6058db65ace02f920; dataset id: paper-dataset-3b08dc110e25f5011e; metric: mean accuracy across eight taxonomy tasks; unit: percent; direction: higher; result ids: paper-result-3b8254d638b7cf840c; paper-result-26353885331e3500b0; paper-result-14f9374f43c9937375; paper-result-37820120325451b083; paper-result-010096d9a694ae3520; paper-result-96a241380ae6ba882a; paper-result-222b0816ea58c393cd; source ids: expansion-p3-glycanml-2405-16206v1; source locator: Table 4, p. 9, Mean Acc (%); context: Fixed backbone; compare adaptation strategies, not standalone foundation models. SugarBase taxonomy: motif-frequency K-means cluster allocation 8:1:1.; caveats: Mean accuracy is an unweighted aggregate of eight taxonomic tasks as reported; keep separate from every individual level.; The six MTL approaches change training, so do not label their scores as the base encoder alone.; review: method: automated_source_review; date: 2026-09-17; id: glycanml-v1-mtl-1-domain; title: GlycanML Domain · Table 4, p. 9; protocol id: paper-protocol-b51570e2bf69ba6542; dataset id: paper-dataset-ce710f303c24cb91c2; metric: accuracy; unit: percent; direction: higher; result ids: paper-result-2aed0631283783ea66; paper-result-944ec4d9e5d591defb; paper-result-976c07b788d0c976b5; paper-result-db9fa9da2f1bc0b208; paper-result-f0d520d129d1982892; paper-result-abafa8cbc9111307ef; paper-result-c1ebf0846e005b0ea7; source ids: expansion-p3-glycanml-2405-16206v1; source locator: Table 4, p. 9, Domain; context: Fixed backbone; compare adaptation strategies, not standalone foundation models. SugarBase taxonomy: motif-frequency K-means cluster allocation 8:1:1.; caveats: Mean accuracy is an unweighted aggregate of eight taxonomic tasks as reported; keep separate from every individual level.; The six MTL approaches change training, so do not label their scores as the base encoder alone.; review: method: automated_source_review; date: 2026-09-17; id: glycanml-v1-mtl-1-kingdom; title: GlycanML Kingdom · Table 4, p. 9; protocol id: paper-protocol-468fdd0f4237c041d5; dataset id: paper-dataset-d7ac4e0285fbaefcab; metric: accuracy; unit: percent; direction: higher; result ids: paper-result-8dea7f67a6307f6e1f; paper-result-89b973f6b24d20d70b; paper-result-7c4ad10784c12bc139; paper-result-853910c368572b5ab8; paper-result-0fd9b263d4d481168c; paper-result-a189d3bce6f0744a64; paper-result-c1581b8b54883511ea; source ids: expansion-p3-glycanml-2405-16206v1; source locator: Table 4, p. 9, Kingdom; context: Fixed backbone; compare adaptation strategies, not standalone foundation models. SugarBase taxonomy: motif-frequency K-means cluster allocation 8:1:1.; caveats: Mean accuracy is an unweighted aggregate of eight taxonomic tasks as reported; keep separate from every individual level.; The six MTL approaches change training, so do not label their scores as the base encoder alone.; review: method: automated_source_review; date: 2026-09-17; id: glycanml-v1-mtl-1-phylum; title: GlycanML Phylum · Table 4, p. 9; protocol id: paper-protocol-a67c6f1eaf73494953; dataset id: paper-dataset-13c71587fb39174bb1; metric: accuracy; unit: percent; direction: higher; result ids: paper-result-778ad7ec5934e369ed; paper-result-d60a1868d6cac6686d; paper-result-3faf014c06fdb5b338; paper-result-6a836283cecd69f216; paper-result-077aee2f6ad33075b7; paper-result-26eb6ff8bcba531860; paper-result-8776fd8149834a3faf; source ids: expansion-p3-glycanml-2405-16206v1; source locator: Table 4, p. 9, Phylum; context: Fixed backbone; compare adaptation strategies, not standalone foundation models. SugarBase taxonomy: motif-frequency K-means cluster allocation 8:1:1.; caveats: Mean accuracy is an unweighted aggregate of eight taxonomic tasks as reported; keep separate from every individual level.; The six MTL approaches change training, so do not label their scores as the base encoder alone.; review: method: automated_source_review; date: 2026-09-17; id: glycanml-v1-mtl-1-class; title: GlycanML Class · Table 4, p. 9; protocol id: paper-protocol-a8799c4d8546b3ff18; dataset id: paper-dataset-f9b7538618a3e1ef5b; metric: accuracy; unit: percent; direction: higher; result ids: paper-result-ed1c70949ef07f3093; paper-result-c4239ca0a78748ef44; paper-result-d6364ea9b2a3f8fdaf; paper-result-34b64c1f78f1731268; paper-result-8f14f53fefa6b454b9; paper-result-42546bacf62f9b3a69; paper-result-7a11fb3230b1f4dcec; source ids: expansion-p3-glycanml-2405-16206v1; source locator: Table 4, p. 9, Class; context: Fixed backbone; compare adaptation strategies, not standalone foundation models. SugarBase taxonomy: motif-frequency K-means cluster allocation 8:1:1.; caveats: Mean accuracy is an unweighted aggregate of eight taxonomic tasks as reported; keep separate from every individual level.; The six MTL approaches change training, so do not label their scores as the base encoder alone.; review: method: automated_source_review; date: 2026-09-17; id: glycanml-v1-mtl-1-order; title: GlycanML Order · Table 4, p. 9; protocol id: paper-protocol-fb9190db65a5f72e10; dataset id: paper-dataset-4298ac38e85e6f8048; metric: accuracy; unit: percent; direction: higher; result ids: paper-result-ac7e4ed00d0e9515da; paper-result-e6c210784feca5b6d4; paper-result-f5553ce1aa89330367; paper-result-57e2eb21a87ce0ad82; paper-result-f8ab2a4236081aaa2d; paper-result-200cf342b98083f8eb; paper-result-6abd4dddb7fdcb0691; source ids: expansion-p3-glycanml-2405-16206v1; source locator: Table 4, p. 9, Order; context: Fixed backbone; compare adaptation strategies, not standalone foundation models. SugarBase taxonomy: motif-frequency K-means cluster allocation 8:1:1.; caveats: Mean accuracy is an unweighted aggregate of eight taxonomic tasks as reported; keep separate from every individual level.; The six MTL approaches change training, so do not label their scores as the base encoder alone.; review: method: automated_source_review; date: 2026-09-17; id: glycanml-v1-mtl-1-family; title: GlycanML Family · Table 4, p. 9; protocol id: paper-protocol-8dca9eabea26dee766; dataset id: paper-dataset-8d5d79e4a6a070bfff; metric: accuracy; unit: percent; direction: higher; result ids: paper-result-e4ad7f8d96df00ea4b; paper-result-7e5eb5e5def1997d2a; paper-result-6189ade0e2553e6a5f; paper-result-850116eec75fde4e16; paper-result-a90400ff944a9dba82; paper-result-4089bd143f1ec302b3; paper-result-b691a9c8c205144c7f; source ids: expansion-p3-glycanml-2405-16206v1; source locator: Table 4, p. 9, Family; context: Fixed backbone; compare adaptation strategies, not standalone foundation models. SugarBase taxonomy: motif-frequency K-means cluster allocation 8:1:1.; caveats: Mean accuracy is an unweighted aggregate of eight taxonomic tasks as reported; keep separate from every individual level.; The six MTL approaches change training, so do not label their scores as the base encoder alone.; review: method: automated_source_review; date: 2026-09-17; id: glycanml-v1-mtl-1-genus; title: GlycanML Genus · Table 4, p. 9; protocol id: paper-protocol-d44b40251c33ecbea1; dataset id: paper-dataset-df6da272a0a4d8de8f; metric: accuracy; unit: percent; direction: higher; result ids: paper-result-fe8185d4fa2fa84e4e; paper-result-14bd301ffa5bdd2ba0; paper-result-6ae0a365e6b33ffa9b; paper-result-fc6174c38a0298468e; paper-result-410dcdb82f6d6c3b0f; paper-result-54ce873648167ddf7f; paper-result-aa50763240e086a7b0; source ids: expansion-p3-glycanml-2405-16206v1; source locator: Table 4, p. 9, Genus; context: Fixed backbone; compare adaptation strategies, not standalone foundation models. SugarBase taxonomy: motif-frequency K-means cluster allocation 8:1:1.; caveats: Mean accuracy is an unweighted aggregate of eight taxonomic tasks as reported; keep separate from every individual level.; The six MTL approaches change training, so do not label their scores as the base encoder alone.; review: method: automated_source_review; date: 2026-09-17; id: glycanml-v1-mtl-1-species; title: GlycanML Species · Table 4, p. 9; protocol id: paper-protocol-0d66c7cabff9d799d5; dataset id: paper-dataset-4c48251022ccb53313; metric: accuracy; unit: percent; direction: higher; result ids: paper-result-723b93ccacba688198; paper-result-ec8c144273ccc0a850; paper-result-928828d65bf0d96282; paper-result-5f765144a6d6d5bdb4; paper-result-dc366357aed61c1324; paper-result-618be13e9cdea80f33; paper-result-e3de6f13924b3f0956; source ids: expansion-p3-glycanml-2405-16206v1; source locator: Table 4, p. 9, Species; context: Fixed backbone; compare adaptation strategies, not standalone foundation models. SugarBase taxonomy: motif-frequency K-means cluster allocation 8:1:1.; caveats: Mean accuracy is an unweighted aggregate of eight taxonomic tasks as reported; keep separate from every individual level.; The six MTL approaches change training, so do not label their scores as the base encoder alone.; review: method: automated_source_review; date: 2026-09-17; id: glycanml-v1-mtl-1-mean-acc; title: GlycanML Mean Acc (%) · Table 4, p. 9; protocol id: paper-protocol-f6058db65ace02f920; dataset id: paper-dataset-3b08dc110e25f5011e; metric: mean accuracy across eight taxonomy tasks; unit: percent; direction: higher; result ids: paper-result-5ebd480b2815e83e3d; paper-result-13c2836aee0f4143ea; paper-result-19abc6d9c3e6eaf449; paper-result-16f47917baa3903d08; paper-result-2d85ed1325988db66d; paper-result-31eba8c1c674dcaf66; paper-result-ecd8e5a52b2ee62bca; source ids: expansion-p3-glycanml-2405-16206v1; source locator: Table 4, p. 9, Mean Acc (%); context: Fixed backbone; compare adaptation strategies, not standalone foundation models. SugarBase taxonomy: motif-frequency K-means cluster allocation 8:1:1.; caveats: Mean accuracy is an unweighted aggregate of eight taxonomic tasks as reported; keep separate from every individual level.; The six MTL approaches change training, so do not label their scores as the base encoder alone.; review: method: automated_source_review; date: 2026-09-17
benchmark research: review date: 2026-09-17; status: complete_comparison_extracted; primary sources: expansion-p3-glycanml-2405-16206v1; inspected locators: Sections 3–5; Tables 1–4; searched queries: GlycanML benchmark 2405.16206; gaps: Paper table does not enumerate trained checkpoint hashes; source configuration names retained.; claim scope: Primary-paper within-study comparison; numerical results not independently reproduced.
historical missing metadata: dataset release: unextracted; metric implementation: unextracted; split manifest: unextracted; version: unextracted
metadata review scope: historical_missing_metadata preserves the original discovery state. Current descriptive evidence and missingness are recorded in profile.facts; numerical-result review is separate.
legacy kinds: benchmark
entity classification: review date: 2026-09-17; rationale: This record identifies the biological prediction question or a suite-specific task, rather than a uniquely fixed evaluated procedure. Preserve its task identity and leave split, model adaptation and scoring details on linked protocols/evaluations.; source ids: src-discovery-glycanml-glycanml; evidence-benchmark-glycanml-bert-species-bert-yaml; evidence-benchmark-glycan-classification-dataset; source locator: Pinned README: Introduction; Experiment configurations; leaderboard; pinned configs/single_task/BERT/species_BERT.yaml, module/custom_datasets/glycan_classification.py (task metric, dataset class and split methods); ambiguities: A paper- or suite-specific task may constrain some inputs or metrics; that alone does not make it interchangeable with a complete versioned protocol. No protocol equivalence is inferred.

Related records

parent: GlycanML
part of: GlycanML
evaluates task: SugarBase taxonomy · Species (GlycanML taxonomy prediction)
evaluates task: SugarBase taxonomy · Kingdom (GlycanML taxonomy prediction)
evaluates task: SugarBase taxonomy · Family (GlycanML taxonomy prediction)
evaluates task: SugarBase taxonomy · Phylum (GlycanML taxonomy prediction)
evaluates task: SugarBase taxonomy · Class (GlycanML taxonomy prediction)
evaluates task: SugarBase taxonomy · Domain (GlycanML taxonomy prediction)
evaluates task: SugarBase taxonomy · Genus (GlycanML taxonomy prediction)
evaluates task: SugarBase taxonomy · Mean Acc (GlycanML taxonomy prediction)
evaluates task: SugarBase taxonomy · Order (GlycanML taxonomy prediction)
subject: GlycanML taxonomy prediction: part of GlycanML

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