scELMo: Embeddings from Language Models are Good Learners for Single-cell Data Analysis
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Evidence
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Evidence table
Inspect claims, sources and review details
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One row per statement and cited source. Multiple citations are not independent evaluations. Shared locators are labelled explicitly.
28 evidence rows matching the loaded filters
| Property and statement | Original source and location | Review and provenance |
|---|---|---|
| attributes.artifact_retrieved_at 2026-09-16T10:41:16.537541+00:00 Source metadata | scELMo: Embeddings from Language Models are Good Learners for Single-cell Data Analysis No field-specific location recorded Version: preprint archived 2025-08-23 | catalogued No individual claim review recorded Audit detailsField: Source artifact SHA-256: Hash scope: Hash scope not separately documented; inspect source record |
| attributes.artifact_sha256 ef75f0d63a567f5e9d7132fd847f44838a82a9741ae55323437e1d1812d86316 Source metadata | scELMo: Embeddings from Language Models are Good Learners for Single-cell Data Analysis No field-specific location recorded Version: preprint archived 2025-08-23 | catalogued No individual claim review recorded Audit detailsField: Source artifact SHA-256: Hash scope: Hash scope not separately documented; inspect source record |
| attributes.artifact_url https://www.ebi.ac.uk/europepmc/webservices/rest/PMC12393277/fullTextXML Source metadata | scELMo: Embeddings from Language Models are Good Learners for Single-cell Data Analysis No field-specific location recorded Version: preprint archived 2025-08-23 | catalogued No individual claim review recorded Audit detailsField: Source artifact SHA-256: Hash scope: Hash scope not separately documented; inspect source record |
| attributes.doi 10.1101/2023.12.07.569910 Source metadata | scELMo: Embeddings from Language Models are Good Learners for Single-cell Data Analysis No field-specific location recorded Version: preprint archived 2025-08-23 | catalogued No individual claim review recorded Audit detailsField: Source artifact SHA-256: Hash scope: Hash scope not separately documented; inspect source record |
| attributes.legacy_paper.doi 10.1101/2023.12.07.569910 Source metadata | scELMo: Embeddings from Language Models are Good Learners for Single-cell Data Analysis No field-specific location recorded Version: preprint archived 2025-08-23 | catalogued No individual claim review recorded Audit detailsField: Source artifact SHA-256: Hash scope: Hash scope not separately documented; inspect source record |
| attributes.legacy_paper.id scelmo-2025 Source metadata | scELMo: Embeddings from Language Models are Good Learners for Single-cell Data Analysis No field-specific location recorded Version: preprint archived 2025-08-23 | catalogued No individual claim review recorded Audit detailsField: Source artifact SHA-256: Hash scope: Hash scope not separately documented; inspect source record |
| attributes.legacy_paper.notes Primary full text via Europe PMC XML; venue: bioRxiv; PMC ID: PMC12393277. Source metadata | scELMo: Embeddings from Language Models are Good Learners for Single-cell Data Analysis No field-specific location recorded Version: preprint archived 2025-08-23 | catalogued No individual claim review recorded Audit detailsField: Source artifact SHA-256: Hash scope: Hash scope not separately documented; inspect source record |
| attributes.legacy_paper.primary_domain cells-tissues Source metadata | scELMo: Embeddings from Language Models are Good Learners for Single-cell Data Analysis No field-specific location recorded Version: preprint archived 2025-08-23 | catalogued No individual claim review recorded Audit detailsField: Source artifact SHA-256: Hash scope: Hash scope not separately documented; inspect source record |
| attributes.legacy_paper.publication_status preprint Source metadata | scELMo: Embeddings from Language Models are Good Learners for Single-cell Data Analysis No field-specific location recorded Version: preprint archived 2025-08-23 | catalogued No individual claim review recorded Audit detailsField: Source artifact SHA-256: Hash scope: Hash scope not separately documented; inspect source record |
| attributes.legacy_paper.retrieved_utc 2026-09-15T23:25:00Z Source metadata | scELMo: Embeddings from Language Models are Good Learners for Single-cell Data Analysis No field-specific location recorded Version: preprint archived 2025-08-23 | catalogued No individual claim review recorded Audit detailsField: Source artifact SHA-256: Hash scope: Hash scope not separately documented; inspect source record |
Sources and history
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Release 2026-09-29-06401fd5b220 · Record review: discovered
Technical metadata and extraction receipts
Stable ID: scelmo-2025
- areas
- cells-tissues
- url
- https://pmc.ncbi.nlm.nih.gov/articles/PMC12393277/
- version
- preprint archived 2025-08-23
- retrieved at
- 2026-09-15T23:25:00Z
- doi
- 10.1101/2023.12.07.569910
- publication status
- preprint
- year
- 2025
- artifact sha256
- ef75f0d63a567f5e9d7132fd847f44838a82a9741ae55323437e1d1812d86316
- artifact url
- https://www.ebi.ac.uk/europepmc/webservices/rest/PMC12393277/fullTextXML
- artifact retrieved at
- 2026-09-16T10:41:16.537541+00:00
- legacy paper
- id: scelmo-2025; title: scELMo: Embeddings from Language Models are Good Learners for Single-cell Data Analysis; year: 2025; publication status: preprint; version: preprint archived 2025-08-23; source url: https://pmc.ncbi.nlm.nih.gov/articles/PMC12393277/; primary domain: cells-tissues; retrieved utc: 2026-09-15T23:25:00Z; notes: Primary full text via Europe PMC XML; venue: bioRxiv; PMC ID: PMC12393277.; doi: 10.1101/2023.12.07.569910
- scope decision
- included
- missing metadata
- licence: not_reported_in_legacy_extract