scope_note: perturbation-ahlmann-20261011-config-cpa
Scope note of CPA (Ahlmann-Eltze et al. 2025)
CPA was not included in the single-perturbation benchmark, as it is not designed to predict the effects of unseen perturbations. (Source Data Fig. 2 Panel C nevertheless has cpa rows; see the evidence concern on that source.)
Descriptive fact transcribed from the pinned source.
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.
6 evidence rows matching the loaded filters
| Property and statement | Original source and location | Review and provenance |
|---|---|---|
| Claimed field scope_note Context-only references | Deep-learning-based gene perturbation effect prediction does not yet outperform simple linear baselines Main text paragraph 16 Version: Nature Methods 22(8):1657, published online 2025-08-04; PMC12328236 full-text XML | not individually reviewed No individual claim review recorded Audit detailsField: Source artifact SHA-256: Hash scope: Hash scope not separately documented; inspect source record |
| Source location Main text paragraph 16 Context-only references | Deep-learning-based gene perturbation effect prediction does not yet outperform simple linear baselines Main text paragraph 16 Version: Nature Methods 22(8):1657, published online 2025-08-04; PMC12328236 full-text XML | not individually reviewed No individual claim review recorded Audit detailsField: Source artifact SHA-256: Hash scope: Hash scope not separately documented; inspect source record |
| Claimed value CPA was not included in the single-perturbation benchmark, as it is not designed to predict the effects of unseen perturbations. (Source Data Fig. 2 Panel C nevertheless has cpa rows; see the evidence concern on that source.) Context-only references | Deep-learning-based gene perturbation effect prediction does not yet outperform simple linear baselines Main text paragraph 16 Version: Nature Methods 22(8):1657, published online 2025-08-04; PMC12328236 full-text XML | not individually reviewed No individual claim review recorded Audit detailsField: Source artifact SHA-256: Hash scope: Hash scope not separately documented; inspect source record |
| Description Descriptive fact transcribed from the pinned source. Context-only references | Deep-learning-based gene perturbation effect prediction does not yet outperform simple linear baselines Main text paragraph 16 Version: Nature Methods 22(8):1657, published online 2025-08-04; PMC12328236 full-text XML | not individually reviewed No individual claim review recorded Audit detailsField: Source artifact SHA-256: Hash scope: Hash scope not separately documented; inspect source record |
| Relationship: subject perturbation-ahlmann-20261011-config-cpa Context-only references | Deep-learning-based gene perturbation effect prediction does not yet outperform simple linear baselines Main text paragraph 16 Version: Nature Methods 22(8):1657, published online 2025-08-04; PMC12328236 full-text XML | not individually reviewed No individual claim review recorded Audit detailsField: Source artifact SHA-256: Hash scope: Hash scope not separately documented; inspect source record |
| Name scope_note: perturbation-ahlmann-20261011-config-cpa Context-only references | Deep-learning-based gene perturbation effect prediction does not yet outperform simple linear baselines Main text paragraph 16 Version: Nature Methods 22(8):1657, published online 2025-08-04; PMC12328236 full-text XML | not individually reviewed No individual claim review recorded Audit detailsField: Source artifact SHA-256: Hash scope: Hash scope not separately documented; inspect source record |
Sources and history
Release 2026-10-22-1b7cf027e4f6 · Record review: source checked
1 source record and release history
- Deep-learning-based gene perturbation effect prediction does not yet outperform simple linear baselines · Original source · Nature Methods 22(8):1657, published online 2025-08-04; PMC12328236 full-text XML
Technical metadata and extraction receipts
Stable ID: perturbation-ahlmann-20261011-claim-cpa-single-excluded
- field
- scope_note
- value
- CPA was not included in the single-perturbation benchmark, as it is not designed to predict the effects of unseen perturbations. (Source Data Fig. 2 Panel C nevertheless has cpa rows; see the evidence concern on that source.)
- source locator
- Main text paragraph 16
- review
- method: source-hash-verification; ai-assisted-source-review; reviewer: claude; reviewer note: Separate Claude (Opus 5.5) review agent, independent of the extractor; no human review claimed; reviewed at: 2026-10-11T03:00:00Z; artifact sha256: dc5ccf62cb51d301f327a3c3e629d60be719bb2aaf02b57fb4999be43a4d95c7; retrieval url: https://www.ebi.ac.uk/europepmc/webservices/rest/PMC12328236/fullTextXML; method note: Compared the claim with the cited paragraph of the re-downloaded article XML.; note: Hand transcription by the extractor. Independent review 2026-10-11: matches the source.
Related records
- subject: CPA (Ahlmann-Eltze et al. 2025)