DiffDock-L (LiPP evaluation): facets.method_types
Facets: method types of DiffDock-L (LiPP evaluation)
["supervised_machine_learning"]
Correct the method type from specialist to supervised_machine_learning (use-case baseline review 2026-10-12, table 2).
Evidence
Source checking verifies the cited claim or transcription. It does not establish independent reproduction.
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.
7 evidence rows matching the loaded filters
| Property and statement | Original source and location | Review and provenance |
|---|---|---|
| Claimed field facets.method_types Context-only references | The LiPP Benchmark Set for Modeling Lipid–Protein Complexes: Comparison of Co-Folding and Docking Methods LiPP: "AI-based docking tool", "Trained on complexes in PDBbind before 2019" Version: PMC13292216.1 | not individually reviewed No individual claim review recorded Audit detailsField: Source artifact SHA-256: Hash scope: Exact retrieved primary paper artifact bytes. |
| Previous value ["specialist"] Context-only references | The LiPP Benchmark Set for Modeling Lipid–Protein Complexes: Comparison of Co-Folding and Docking Methods LiPP: "AI-based docking tool", "Trained on complexes in PDBbind before 2019" Version: PMC13292216.1 | not individually reviewed No individual claim review recorded Audit detailsField: Source artifact SHA-256: Hash scope: Exact retrieved primary paper artifact bytes. |
| Source location LiPP: "AI-based docking tool", "Trained on complexes in PDBbind before 2019" Context-only references | The LiPP Benchmark Set for Modeling Lipid–Protein Complexes: Comparison of Co-Folding and Docking Methods LiPP: "AI-based docking tool", "Trained on complexes in PDBbind before 2019" Version: PMC13292216.1 | not individually reviewed No individual claim review recorded Audit detailsField: Source artifact SHA-256: Hash scope: Exact retrieved primary paper artifact bytes. |
| Claimed value ["supervised_machine_learning"] Context-only references | The LiPP Benchmark Set for Modeling Lipid–Protein Complexes: Comparison of Co-Folding and Docking Methods LiPP: "AI-based docking tool", "Trained on complexes in PDBbind before 2019" Version: PMC13292216.1 | not individually reviewed No individual claim review recorded Audit detailsField: Source artifact SHA-256: Hash scope: Exact retrieved primary paper artifact bytes. |
| Description Correct the method type from specialist to supervised_machine_learning (use-case baseline review 2026-10-12, table 2). Context-only references | The LiPP Benchmark Set for Modeling Lipid–Protein Complexes: Comparison of Co-Folding and Docking Methods LiPP: "AI-based docking tool", "Trained on complexes in PDBbind before 2019" Version: PMC13292216.1 | not individually reviewed No individual claim review recorded Audit detailsField: Source artifact SHA-256: Hash scope: Exact retrieved primary paper artifact bytes. |
| Relationship: subject lit-structure-20261011-lipp-config-diffdock-l Context-only references | The LiPP Benchmark Set for Modeling Lipid–Protein Complexes: Comparison of Co-Folding and Docking Methods LiPP: "AI-based docking tool", "Trained on complexes in PDBbind before 2019" Version: PMC13292216.1 | not individually reviewed No individual claim review recorded Audit detailsField: Source artifact SHA-256: Hash scope: Exact retrieved primary paper artifact bytes. |
| Name DiffDock-L (LiPP evaluation): facets.method_types Context-only references | The LiPP Benchmark Set for Modeling Lipid–Protein Complexes: Comparison of Co-Folding and Docking Methods LiPP: "AI-based docking tool", "Trained on complexes in PDBbind before 2019" Version: PMC13292216.1 | not individually reviewed No individual claim review recorded Audit detailsField: Source artifact SHA-256: Hash scope: Exact retrieved primary paper artifact bytes. |
Sources and history
Release 2026-10-23-5347695500ed · Record review: source checked
1 source record and release history
- The LiPP Benchmark Set for Modeling Lipid–Protein Complexes: Comparison of Co-Folding and Docking Methods · Original source · PMC13292216.1
Technical metadata and extraction receipts
Stable ID: metadata-correction-tags-20261012-7a1e3080438e1e4a5863
- field
- facets.method_types
- previous value
- ["specialist"]
- value
- ["supervised_machine_learning"]
- source locator
- LiPP: "AI-based docking tool", "Trained on complexes in PDBbind before 2019"
- review
- method: source-hash-verification; automated-source-review; reviewer: claude; reviewer note: Separate Claude review agent, independent of the agent that prepared the corrections; no human review claimed; date: 2026-10-12; method note: Compared previous_value with origin/main and the store; re-retrieved the source (matches the source record) and read the passage in source_locator; checked the new value against the method-type definitions in data/vocab/method-type.ttl.; artifact sha256: 6ff34f2f709a14858a3753abf9f8f6efa1e7e3c351f15c70cf64264193a9414e; retrieval url: https://www.ebi.ac.uk/europepmc/webservices/rest/PMC13292216/fullTextXML; note: Correct the method type from specialist to supervised_machine_learning (use-case baseline review 2026-10-12, table 2). Evidence from docs/reviews/use-cases/baselines-2026-10-12.md. Independent review 2026-10-12: approved. See docs/reviews/2026-10-12-method-type-corrections.md.
Related records
- subject: DiffDock-L (LiPP evaluation)