cross_source_consistency: tgtval-20261009-protocol-gupta2025-cumulative-hits-round5
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.
12 evidence rows matching the loaded filters
| Property and statement | Original source and location | Review and provenance |
|---|---|---|
| attributes.field cross_source_consistency Context-only references | LLMs for Bayesian Optimization in Scientific Domains: Are We There Yet? Gupta et al. 2025 Tables 1 and 2 against Roohani et al. 2025 Table 1 Shared locator for this statement’s cited sources; not a separate locator for each citation. Version: Findings of the Association for Computational Linguistics: EMNLP 2025, pages 15482-15510 | not individually reviewed No individual claim review recorded Audit detailsField: Source artifact SHA-256: Hash scope: pdftotext -layout text layer, parsed by extract/extract_target_validation.py |
| attributes.field cross_source_consistency Context-only references | BioDiscoveryAgent: An AI Agent for Designing Genetic Perturbation Experiments Gupta et al. 2025 Tables 1 and 2 against Roohani et al. 2025 Table 1 Shared locator for this statement’s cited sources; not a separate locator for each citation. Version: arXiv:2405.17631 version 3, updated 2025-03-09; published as a conference paper at ICLR 2025 | not individually reviewed No individual claim review recorded Audit detailsField: Source artifact SHA-256: Hash scope: pdftotext -layout text layer, parsed by extract/extract_target_validation.py |
| attributes.source_locator Gupta et al. 2025 Tables 1 and 2 against Roohani et al. 2025 Table 1 Context-only references | LLMs for Bayesian Optimization in Scientific Domains: Are We There Yet? Gupta et al. 2025 Tables 1 and 2 against Roohani et al. 2025 Table 1 Shared locator for this statement’s cited sources; not a separate locator for each citation. Version: Findings of the Association for Computational Linguistics: EMNLP 2025, pages 15482-15510 | not individually reviewed No individual claim review recorded Audit detailsField: Source artifact SHA-256: Hash scope: pdftotext -layout text layer, parsed by extract/extract_target_validation.py |
| attributes.source_locator Gupta et al. 2025 Tables 1 and 2 against Roohani et al. 2025 Table 1 Context-only references | BioDiscoveryAgent: An AI Agent for Designing Genetic Perturbation Experiments Gupta et al. 2025 Tables 1 and 2 against Roohani et al. 2025 Table 1 Shared locator for this statement’s cited sources; not a separate locator for each citation. Version: arXiv:2405.17631 version 3, updated 2025-03-09; published as a conference paper at ICLR 2025 | not individually reviewed No individual claim review recorded Audit detailsField: Source artifact SHA-256: Hash scope: pdftotext -layout text layer, parsed by extract/extract_target_validation.py |
| attributes.value The 'BDA (Reported Numbers)' row divided by the printed ground-truth hit counts reproduces the all-gene hit ratios of Roohani et al. 2025 Table 1 for Claude 3.5 Sonnet to the printed rounding: IL2 68.01/654 = 0.104 against 0.104, IFNG 87.4/920 = 0.095 against 0.095, Carnevale 39.6/943 = 0.042 against 0.042 and Sanchez 60.72/924 = 0.0657 against 0.066. This indicates that both sources score against the same screens and the same hit sets, and that the row converts the original results rather than reporting a new run. The conversion used values more precise than Roohani Table 1 prints: 0.066 x 924 = 60.98, while the row prints 60.72. Checked during extraction; no value was altered. Context-only references | LLMs for Bayesian Optimization in Scientific Domains: Are We There Yet? Gupta et al. 2025 Tables 1 and 2 against Roohani et al. 2025 Table 1 Shared locator for this statement’s cited sources; not a separate locator for each citation. Version: Findings of the Association for Computational Linguistics: EMNLP 2025, pages 15482-15510 | not individually reviewed No individual claim review recorded Audit detailsField: Source artifact SHA-256: Hash scope: pdftotext -layout text layer, parsed by extract/extract_target_validation.py |
| attributes.value The 'BDA (Reported Numbers)' row divided by the printed ground-truth hit counts reproduces the all-gene hit ratios of Roohani et al. 2025 Table 1 for Claude 3.5 Sonnet to the printed rounding: IL2 68.01/654 = 0.104 against 0.104, IFNG 87.4/920 = 0.095 against 0.095, Carnevale 39.6/943 = 0.042 against 0.042 and Sanchez 60.72/924 = 0.0657 against 0.066. This indicates that both sources score against the same screens and the same hit sets, and that the row converts the original results rather than reporting a new run. The conversion used values more precise than Roohani Table 1 prints: 0.066 x 924 = 60.98, while the row prints 60.72. Checked during extraction; no value was altered. Context-only references | BioDiscoveryAgent: An AI Agent for Designing Genetic Perturbation Experiments Gupta et al. 2025 Tables 1 and 2 against Roohani et al. 2025 Table 1 Shared locator for this statement’s cited sources; not a separate locator for each citation. Version: arXiv:2405.17631 version 3, updated 2025-03-09; published as a conference paper at ICLR 2025 | not individually reviewed No individual claim review recorded Audit detailsField: Source artifact SHA-256: Hash scope: pdftotext -layout text layer, parsed by extract/extract_target_validation.py |
| description Descriptive fact transcribed from the pinned source. Context-only references | LLMs for Bayesian Optimization in Scientific Domains: Are We There Yet? Gupta et al. 2025 Tables 1 and 2 against Roohani et al. 2025 Table 1 Shared locator for this statement’s cited sources; not a separate locator for each citation. Version: Findings of the Association for Computational Linguistics: EMNLP 2025, pages 15482-15510 | not individually reviewed No individual claim review recorded Audit detailsField: Source artifact SHA-256: Hash scope: pdftotext -layout text layer, parsed by extract/extract_target_validation.py |
| description Descriptive fact transcribed from the pinned source. Context-only references | BioDiscoveryAgent: An AI Agent for Designing Genetic Perturbation Experiments Gupta et al. 2025 Tables 1 and 2 against Roohani et al. 2025 Table 1 Shared locator for this statement’s cited sources; not a separate locator for each citation. Version: arXiv:2405.17631 version 3, updated 2025-03-09; published as a conference paper at ICLR 2025 | not individually reviewed No individual claim review recorded Audit detailsField: Source artifact SHA-256: Hash scope: pdftotext -layout text layer, parsed by extract/extract_target_validation.py |
| Relationship: subject tgtval-20261009-protocol-gupta2025-cumulative-hits-round5 Context-only references | LLMs for Bayesian Optimization in Scientific Domains: Are We There Yet? Gupta et al. 2025 Tables 1 and 2 against Roohani et al. 2025 Table 1 Shared locator for this statement’s cited sources; not a separate locator for each citation. Version: Findings of the Association for Computational Linguistics: EMNLP 2025, pages 15482-15510 | not individually reviewed No individual claim review recorded Audit detailsField: Source artifact SHA-256: Hash scope: pdftotext -layout text layer, parsed by extract/extract_target_validation.py |
| Relationship: subject tgtval-20261009-protocol-gupta2025-cumulative-hits-round5 Context-only references | BioDiscoveryAgent: An AI Agent for Designing Genetic Perturbation Experiments Gupta et al. 2025 Tables 1 and 2 against Roohani et al. 2025 Table 1 Shared locator for this statement’s cited sources; not a separate locator for each citation. Version: arXiv:2405.17631 version 3, updated 2025-03-09; published as a conference paper at ICLR 2025 | not individually reviewed No individual claim review recorded Audit detailsField: Source artifact SHA-256: Hash scope: pdftotext -layout text layer, parsed by extract/extract_target_validation.py |
Sources and history
Release 2026-10-10-6e93f504adfc · Record review: source checked
2 source records and release history
- LLMs for Bayesian Optimization in Scientific Domains: Are We There Yet? · Original source · Findings of the Association for Computational Linguistics: EMNLP 2025, pages 15482-15510
- BioDiscoveryAgent: An AI Agent for Designing Genetic Perturbation Experiments · Original source · arXiv:2405.17631 version 3, updated 2025-03-09; published as a conference paper at ICLR 2025
Technical metadata and extraction receipts
Stable ID: tgtval-20261009-claim-gupta2025-reported-numbers-conversion
- field
- cross_source_consistency
- value
- The 'BDA (Reported Numbers)' row divided by the printed ground-truth hit counts reproduces the all-gene hit ratios of Roohani et al. 2025 Table 1 for Claude 3.5 Sonnet to the printed rounding: IL2 68.01/654 = 0.104 against 0.104, IFNG 87.4/920 = 0.095 against 0.095, Carnevale 39.6/943 = 0.042 against 0.042 and Sanchez 60.72/924 = 0.0657 against 0.066. This indicates that both sources score against the same screens and the same hit sets, and that the row converts the original results rather than reporting a new run. The conversion used values more precise than Roohani Table 1 prints: 0.066 x 924 = 60.98, while the row prints 60.72. Checked during extraction; no value was altered.
- source locator
- Gupta et al. 2025 Tables 1 and 2 against Roohani et al. 2025 Table 1
- review
- method: source-hash-verification; ai-assisted-source-review; reviewer: claude; reviewer note: Separate Claude review agent, independent of the extractor; no human review claimed; reviewed at: 2026-10-09T21:22:54Z; artifact sha256: 7dda0b590f2736b7d30e48b97167a0868a2b4bde253589d8aff7929d01506ff9; retrieval url: https://aclanthology.org/2025.findings-emnlp.838.pdf; note: Hand transcription from the source text. Independent review 2026-10-09: checked against the re-downloaded PDF text; see docs/reviews/use-cases/therapeutic-target-validation-2026-10-09.md.