Configuration

GPT-4o, direct prompting, mean over prompting strategies (Song et al. 2025)

Evidence concern: excluded from comparisons

Appendix A.2 describes the random baseline as predicting hit with probability equal to 'the overall proportion of actual hits in the specific benchmark dataset'. With one pooled rate (485 of 1,175 = 41.3%, Table 3) the expected PPV, sensitivity, F1 and FPR are all about 0.41 and specificity and NPV about 0.59. Table 8 prints PPV 0.47, sensitivity 0.46, F1 0.47, FPR 0.36, specificity 0.64 and NPV 0.63. With each screen's own hit rate (Table 3) the expected values are 0.47, 0.47, 0.47, 0.37, 0.63 and 0.63, which match. One draw at the pooled rate would need sensitivity about 2 standard errors above and FPR about 3 below expectation at once. The baseline therefore probably uses per-screen hit rates, which is information a screen designer would not have before the screen. The expected values were computed by the reviewer from Table 3 counts and are not stored as results. Treat the random row as a per-screen-prevalence classifier, not a matched random-selection comparator.Virtual CRISPR: Can LLMs Predict CRISPR Screen Results? · Appendix A.2; Table 3; Table 1, row "Random Baseline"; Table 8, row "Random N/A N/A"

GPT-4o answering yes/no CRISPR screen questions by direct prompting, summarised over zero-shot, few-shot and chain-of-thought prompt variants.

2 evaluations · 10 results

Overview

An explanatory profile has not yet been reviewed for this record.

Consult the linked sources for architecture or protocol details. Missing evidence is not evidence of a missing capability.

Evaluations and results

2 evaluations · 10 results. Different protocols are not a single leaderboard.

Sorted by AUPRC (Difficult Benchmark) (higher is better). The best value in each column is highlighted. Decimals are rounded for display; each value links to the printed value and its source.

Tested configuration	Protocol and dataset	AUPRC (Difficult Benchmark)	AUROC (Difficult Benchmark)	F1 (Difficult Benchmark)	F1 (Simple Benchmark)	False positive rate (Difficult Benchmark)	False positive rate (Simple Benchmark)	Negative predictive value (Difficult Benchmark)	Precision (Difficult Benchmark)	Recall (Difficult Benchmark)	Specificity (Difficult Benchmark)
GPT-4o, direct prompting, mean over prompting strategies (Song et al. 2025)Evaluation	Virtual CRISPR Difficult Benchmark: hit prediction for complex phenotypes, Virtual CRISPR Difficult Benchmark	N/A	N/A	0.35	Not reported	0.79	Not reported	0.23	0.32	0.41	0.21
GPT-4o, direct prompting, mean over prompting strategies (Song et al. 2025)Evaluation	Virtual CRISPR Simple Benchmark: yes/no prediction of a screen hit, Virtual CRISPR Simple Benchmark	Not reported	Not reported	Not reported	0.47 ± 0.06	Not reported	0.22 ± 0.06	Not reported	Not reported	Not reported	Not reported

All 10 result rows with coverage, uncertainty and sources

Filter evaluations

Tested model or methodProtocolDatasetEvaluation setupMetricEvidence origin

Applied filters: All linked evaluations

Exact evaluated configurations and original reported results
Tested configuration	Protocol and dataset	Finding	Evidence and details
Configuration: GPT-4o, direct prompting, mean over prompting strategies (Song et al. 2025)	Protocol: Virtual CRISPR Difficult Benchmark: hit prediction for complex phenotypes Dataset: Virtual CRISPR Difficult Benchmark	N/A AUPRC (Difficult Benchmark) unitless · higher Uncertainty: Not applicable Coverage: Not reported scored / Not reported eligible	Independent external evaluation · Source checked Evidence concern: excluded from comparisons Appendix A.2 describes the random baseline as predicting hit with probability equal to 'the overall proportion of actual hits in the specific benchmark dataset'. With one pooled rate (485 of 1,175 = 41.3%, Table 3) the expected PPV, sensitivity, F1 and FPR are all about 0.41 and specificity and NPV about 0.59. Table 8 prints PPV 0.47, sensitivity 0.46, F1 0.47, FPR 0.36, specificity 0.64 and NPV 0.63. With each screen's own hit rate (Table 3) the expected values are 0.47, 0.47, 0.47, 0.37, 0.63 and 0.63, which match. One draw at the pooled rate would need sensitivity about 2 standard errors above and FPR about 3 below expectation at once. The baseline therefore probably uses per-screen hit rates, which is information a screen designer would not have before the screen. The expected values were computed by the reviewer from Table 3 counts and are not stored as results. Treat the random row as a per-screen-prevalence classifier, not a matched random-selection comparator.Virtual CRISPR: Can LLMs Predict CRISPR Screen Results? · Appendix A.2; Table 3; Table 1, row "Random Baseline"; Table 8, row "Random N/A N/A" Methods, coverage and source GPT-4o (prompting) on the Virtual CRISPR Difficult Benchmark pheno-20261022-protocol-song2025-difficult-hit-classification Aggregation: Not reported Virtual CRISPR: Can LLMs Predict CRISPR Screen Results? · Table 2, row "GPT-4o (prompting)", column "AUPRC"
Configuration: GPT-4o, direct prompting, mean over prompting strategies (Song et al. 2025)	Protocol: Virtual CRISPR Difficult Benchmark: hit prediction for complex phenotypes Dataset: Virtual CRISPR Difficult Benchmark	N/A AUROC (Difficult Benchmark) unitless · higher Uncertainty: Not applicable Coverage: Not reported scored / Not reported eligible	Independent external evaluation · Source checked Evidence concern: excluded from comparisons Appendix A.2 describes the random baseline as predicting hit with probability equal to 'the overall proportion of actual hits in the specific benchmark dataset'. With one pooled rate (485 of 1,175 = 41.3%, Table 3) the expected PPV, sensitivity, F1 and FPR are all about 0.41 and specificity and NPV about 0.59. Table 8 prints PPV 0.47, sensitivity 0.46, F1 0.47, FPR 0.36, specificity 0.64 and NPV 0.63. With each screen's own hit rate (Table 3) the expected values are 0.47, 0.47, 0.47, 0.37, 0.63 and 0.63, which match. One draw at the pooled rate would need sensitivity about 2 standard errors above and FPR about 3 below expectation at once. The baseline therefore probably uses per-screen hit rates, which is information a screen designer would not have before the screen. The expected values were computed by the reviewer from Table 3 counts and are not stored as results. Treat the random row as a per-screen-prevalence classifier, not a matched random-selection comparator.Virtual CRISPR: Can LLMs Predict CRISPR Screen Results? · Appendix A.2; Table 3; Table 1, row "Random Baseline"; Table 8, row "Random N/A N/A" Methods, coverage and source GPT-4o (prompting) on the Virtual CRISPR Difficult Benchmark pheno-20261022-protocol-song2025-difficult-hit-classification Aggregation: Not reported Virtual CRISPR: Can LLMs Predict CRISPR Screen Results? · Table 2, row "GPT-4o (prompting)", column "AUROC"
Configuration: GPT-4o, direct prompting, mean over prompting strategies (Song et al. 2025)	Protocol: Virtual CRISPR Difficult Benchmark: hit prediction for complex phenotypes Dataset: Virtual CRISPR Difficult Benchmark	0.35 F1 (Difficult Benchmark) fraction · higher Uncertainty: ± 0.17 (type not stated). Printed on the line below the row; the caption says results are aggregated across prompting strategies but does not name the statistic. Table 1 uses mean and standard deviation for the same kind of aggregation. Coverage: Not reported scored / Not reported eligible	Independent external evaluation · Source checked Evidence concern: excluded from comparisons Appendix A.2 describes the random baseline as predicting hit with probability equal to 'the overall proportion of actual hits in the specific benchmark dataset'. With one pooled rate (485 of 1,175 = 41.3%, Table 3) the expected PPV, sensitivity, F1 and FPR are all about 0.41 and specificity and NPV about 0.59. Table 8 prints PPV 0.47, sensitivity 0.46, F1 0.47, FPR 0.36, specificity 0.64 and NPV 0.63. With each screen's own hit rate (Table 3) the expected values are 0.47, 0.47, 0.47, 0.37, 0.63 and 0.63, which match. One draw at the pooled rate would need sensitivity about 2 standard errors above and FPR about 3 below expectation at once. The baseline therefore probably uses per-screen hit rates, which is information a screen designer would not have before the screen. The expected values were computed by the reviewer from Table 3 counts and are not stored as results. Treat the random row as a per-screen-prevalence classifier, not a matched random-selection comparator.Virtual CRISPR: Can LLMs Predict CRISPR Screen Results? · Appendix A.2; Table 3; Table 1, row "Random Baseline"; Table 8, row "Random N/A N/A" Methods, coverage and source GPT-4o (prompting) on the Virtual CRISPR Difficult Benchmark pheno-20261022-protocol-song2025-difficult-hit-classification Aggregation: Not reported Virtual CRISPR: Can LLMs Predict CRISPR Screen Results? · Table 2, row "GPT-4o (prompting)", column "F1"
Configuration: GPT-4o, direct prompting, mean over prompting strategies (Song et al. 2025)	Protocol: Virtual CRISPR Difficult Benchmark: hit prediction for complex phenotypes Dataset: Virtual CRISPR Difficult Benchmark	0.79 False positive rate (Difficult Benchmark) fraction · lower Uncertainty: ± 0.19 (type not stated). Printed on the line below the row; the caption says results are aggregated across prompting strategies but does not name the statistic. Table 1 uses mean and standard deviation for the same kind of aggregation. Coverage: Not reported scored / Not reported eligible	Independent external evaluation · Source checked Evidence concern: excluded from comparisons Appendix A.2 describes the random baseline as predicting hit with probability equal to 'the overall proportion of actual hits in the specific benchmark dataset'. With one pooled rate (485 of 1,175 = 41.3%, Table 3) the expected PPV, sensitivity, F1 and FPR are all about 0.41 and specificity and NPV about 0.59. Table 8 prints PPV 0.47, sensitivity 0.46, F1 0.47, FPR 0.36, specificity 0.64 and NPV 0.63. With each screen's own hit rate (Table 3) the expected values are 0.47, 0.47, 0.47, 0.37, 0.63 and 0.63, which match. One draw at the pooled rate would need sensitivity about 2 standard errors above and FPR about 3 below expectation at once. The baseline therefore probably uses per-screen hit rates, which is information a screen designer would not have before the screen. The expected values were computed by the reviewer from Table 3 counts and are not stored as results. Treat the random row as a per-screen-prevalence classifier, not a matched random-selection comparator.Virtual CRISPR: Can LLMs Predict CRISPR Screen Results? · Appendix A.2; Table 3; Table 1, row "Random Baseline"; Table 8, row "Random N/A N/A" Methods, coverage and source GPT-4o (prompting) on the Virtual CRISPR Difficult Benchmark pheno-20261022-protocol-song2025-difficult-hit-classification Aggregation: Not reported Virtual CRISPR: Can LLMs Predict CRISPR Screen Results? · Table 2, row "GPT-4o (prompting)", column "FPR"
Configuration: GPT-4o, direct prompting, mean over prompting strategies (Song et al. 2025)	Protocol: Virtual CRISPR Difficult Benchmark: hit prediction for complex phenotypes Dataset: Virtual CRISPR Difficult Benchmark	0.23 Negative predictive value (Difficult Benchmark) fraction · higher Uncertainty: ± 0.13 (type not stated). Printed on the line below the row; the caption says results are aggregated across prompting strategies but does not name the statistic. Table 1 uses mean and standard deviation for the same kind of aggregation. Coverage: Not reported scored / Not reported eligible	Independent external evaluation · Source checked Evidence concern: excluded from comparisons Appendix A.2 describes the random baseline as predicting hit with probability equal to 'the overall proportion of actual hits in the specific benchmark dataset'. With one pooled rate (485 of 1,175 = 41.3%, Table 3) the expected PPV, sensitivity, F1 and FPR are all about 0.41 and specificity and NPV about 0.59. Table 8 prints PPV 0.47, sensitivity 0.46, F1 0.47, FPR 0.36, specificity 0.64 and NPV 0.63. With each screen's own hit rate (Table 3) the expected values are 0.47, 0.47, 0.47, 0.37, 0.63 and 0.63, which match. One draw at the pooled rate would need sensitivity about 2 standard errors above and FPR about 3 below expectation at once. The baseline therefore probably uses per-screen hit rates, which is information a screen designer would not have before the screen. The expected values were computed by the reviewer from Table 3 counts and are not stored as results. Treat the random row as a per-screen-prevalence classifier, not a matched random-selection comparator.Virtual CRISPR: Can LLMs Predict CRISPR Screen Results? · Appendix A.2; Table 3; Table 1, row "Random Baseline"; Table 8, row "Random N/A N/A" Methods, coverage and source GPT-4o (prompting) on the Virtual CRISPR Difficult Benchmark pheno-20261022-protocol-song2025-difficult-hit-classification Aggregation: Not reported Virtual CRISPR: Can LLMs Predict CRISPR Screen Results? · Table 2, row "GPT-4o (prompting)", column "NPV"
Configuration: GPT-4o, direct prompting, mean over prompting strategies (Song et al. 2025)	Protocol: Virtual CRISPR Difficult Benchmark: hit prediction for complex phenotypes Dataset: Virtual CRISPR Difficult Benchmark	0.32 Precision (Difficult Benchmark) fraction · higher Uncertainty: ± 0.14 (type not stated). Printed on the line below the row; the caption says results are aggregated across prompting strategies but does not name the statistic. Table 1 uses mean and standard deviation for the same kind of aggregation. Coverage: Not reported scored / Not reported eligible	Independent external evaluation · Source checked Evidence concern: excluded from comparisons Appendix A.2 describes the random baseline as predicting hit with probability equal to 'the overall proportion of actual hits in the specific benchmark dataset'. With one pooled rate (485 of 1,175 = 41.3%, Table 3) the expected PPV, sensitivity, F1 and FPR are all about 0.41 and specificity and NPV about 0.59. Table 8 prints PPV 0.47, sensitivity 0.46, F1 0.47, FPR 0.36, specificity 0.64 and NPV 0.63. With each screen's own hit rate (Table 3) the expected values are 0.47, 0.47, 0.47, 0.37, 0.63 and 0.63, which match. One draw at the pooled rate would need sensitivity about 2 standard errors above and FPR about 3 below expectation at once. The baseline therefore probably uses per-screen hit rates, which is information a screen designer would not have before the screen. The expected values were computed by the reviewer from Table 3 counts and are not stored as results. Treat the random row as a per-screen-prevalence classifier, not a matched random-selection comparator.Virtual CRISPR: Can LLMs Predict CRISPR Screen Results? · Appendix A.2; Table 3; Table 1, row "Random Baseline"; Table 8, row "Random N/A N/A" Methods, coverage and source GPT-4o (prompting) on the Virtual CRISPR Difficult Benchmark pheno-20261022-protocol-song2025-difficult-hit-classification Aggregation: Not reported Virtual CRISPR: Can LLMs Predict CRISPR Screen Results? · Table 2, row "GPT-4o (prompting)", column "PPV"
Configuration: GPT-4o, direct prompting, mean over prompting strategies (Song et al. 2025)	Protocol: Virtual CRISPR Difficult Benchmark: hit prediction for complex phenotypes Dataset: Virtual CRISPR Difficult Benchmark	0.41 Recall (Difficult Benchmark) fraction · higher Uncertainty: ± 0.22 (type not stated). Printed on the line below the row; the caption says results are aggregated across prompting strategies but does not name the statistic. Table 1 uses mean and standard deviation for the same kind of aggregation. Coverage: Not reported scored / Not reported eligible	Independent external evaluation · Source checked Evidence concern: excluded from comparisons Appendix A.2 describes the random baseline as predicting hit with probability equal to 'the overall proportion of actual hits in the specific benchmark dataset'. With one pooled rate (485 of 1,175 = 41.3%, Table 3) the expected PPV, sensitivity, F1 and FPR are all about 0.41 and specificity and NPV about 0.59. Table 8 prints PPV 0.47, sensitivity 0.46, F1 0.47, FPR 0.36, specificity 0.64 and NPV 0.63. With each screen's own hit rate (Table 3) the expected values are 0.47, 0.47, 0.47, 0.37, 0.63 and 0.63, which match. One draw at the pooled rate would need sensitivity about 2 standard errors above and FPR about 3 below expectation at once. The baseline therefore probably uses per-screen hit rates, which is information a screen designer would not have before the screen. The expected values were computed by the reviewer from Table 3 counts and are not stored as results. Treat the random row as a per-screen-prevalence classifier, not a matched random-selection comparator.Virtual CRISPR: Can LLMs Predict CRISPR Screen Results? · Appendix A.2; Table 3; Table 1, row "Random Baseline"; Table 8, row "Random N/A N/A" Methods, coverage and source GPT-4o (prompting) on the Virtual CRISPR Difficult Benchmark pheno-20261022-protocol-song2025-difficult-hit-classification Aggregation: Not reported Virtual CRISPR: Can LLMs Predict CRISPR Screen Results? · Table 2, row "GPT-4o (prompting)", column "Sens."
Configuration: GPT-4o, direct prompting, mean over prompting strategies (Song et al. 2025)	Protocol: Virtual CRISPR Difficult Benchmark: hit prediction for complex phenotypes Dataset: Virtual CRISPR Difficult Benchmark	0.21 Specificity (Difficult Benchmark) fraction · higher Uncertainty: ± 0.19 (type not stated). Printed on the line below the row; the caption says results are aggregated across prompting strategies but does not name the statistic. Table 1 uses mean and standard deviation for the same kind of aggregation. Coverage: Not reported scored / Not reported eligible	Independent external evaluation · Source checked Evidence concern: excluded from comparisons Appendix A.2 describes the random baseline as predicting hit with probability equal to 'the overall proportion of actual hits in the specific benchmark dataset'. With one pooled rate (485 of 1,175 = 41.3%, Table 3) the expected PPV, sensitivity, F1 and FPR are all about 0.41 and specificity and NPV about 0.59. Table 8 prints PPV 0.47, sensitivity 0.46, F1 0.47, FPR 0.36, specificity 0.64 and NPV 0.63. With each screen's own hit rate (Table 3) the expected values are 0.47, 0.47, 0.47, 0.37, 0.63 and 0.63, which match. One draw at the pooled rate would need sensitivity about 2 standard errors above and FPR about 3 below expectation at once. The baseline therefore probably uses per-screen hit rates, which is information a screen designer would not have before the screen. The expected values were computed by the reviewer from Table 3 counts and are not stored as results. Treat the random row as a per-screen-prevalence classifier, not a matched random-selection comparator.Virtual CRISPR: Can LLMs Predict CRISPR Screen Results? · Appendix A.2; Table 3; Table 1, row "Random Baseline"; Table 8, row "Random N/A N/A" Methods, coverage and source GPT-4o (prompting) on the Virtual CRISPR Difficult Benchmark pheno-20261022-protocol-song2025-difficult-hit-classification Aggregation: Not reported Virtual CRISPR: Can LLMs Predict CRISPR Screen Results? · Table 2, row "GPT-4o (prompting)", column "Spec."
Configuration: GPT-4o, direct prompting, mean over prompting strategies (Song et al. 2025)	Protocol: Virtual CRISPR Simple Benchmark: yes/no prediction of a screen hit Dataset: Virtual CRISPR Simple Benchmark	0.47 ± 0.06 F1 (Simple Benchmark) fraction · higher Uncertainty: SD 0.06. Caption: mean and std. dev. across up to 10 combinations of CoT and few-shot prompting; the number of combinations per model is not printed Coverage: Not reported scored / Not reported eligible	Independent external evaluation · Source checked Evidence concern: excluded from comparisons Appendix A.2 describes the random baseline as predicting hit with probability equal to 'the overall proportion of actual hits in the specific benchmark dataset'. With one pooled rate (485 of 1,175 = 41.3%, Table 3) the expected PPV, sensitivity, F1 and FPR are all about 0.41 and specificity and NPV about 0.59. Table 8 prints PPV 0.47, sensitivity 0.46, F1 0.47, FPR 0.36, specificity 0.64 and NPV 0.63. With each screen's own hit rate (Table 3) the expected values are 0.47, 0.47, 0.47, 0.37, 0.63 and 0.63, which match. One draw at the pooled rate would need sensitivity about 2 standard errors above and FPR about 3 below expectation at once. The baseline therefore probably uses per-screen hit rates, which is information a screen designer would not have before the screen. The expected values were computed by the reviewer from Table 3 counts and are not stored as results. Treat the random row as a per-screen-prevalence classifier, not a matched random-selection comparator.Virtual CRISPR: Can LLMs Predict CRISPR Screen Results? · Appendix A.2; Table 3; Table 1, row "Random Baseline"; Table 8, row "Random N/A N/A" Methods, coverage and source GPT-4o (direct prompting, mean over strategies) on the Virtual CRISPR Simple Benchmark pheno-20261022-protocol-song2025-simple-hit-classification Aggregation: Not reported Virtual CRISPR: Can LLMs Predict CRISPR Screen Results? · Table 1, row "GPT-4o", column "F1 Score"
Configuration: GPT-4o, direct prompting, mean over prompting strategies (Song et al. 2025)	Protocol: Virtual CRISPR Simple Benchmark: yes/no prediction of a screen hit Dataset: Virtual CRISPR Simple Benchmark	0.22 ± 0.06 False positive rate (Simple Benchmark) fraction · lower Uncertainty: SD 0.06. Caption: mean and std. dev. across up to 10 combinations of CoT and few-shot prompting; the number of combinations per model is not printed Coverage: Not reported scored / Not reported eligible	Independent external evaluation · Source checked Evidence concern: excluded from comparisons Appendix A.2 describes the random baseline as predicting hit with probability equal to 'the overall proportion of actual hits in the specific benchmark dataset'. With one pooled rate (485 of 1,175 = 41.3%, Table 3) the expected PPV, sensitivity, F1 and FPR are all about 0.41 and specificity and NPV about 0.59. Table 8 prints PPV 0.47, sensitivity 0.46, F1 0.47, FPR 0.36, specificity 0.64 and NPV 0.63. With each screen's own hit rate (Table 3) the expected values are 0.47, 0.47, 0.47, 0.37, 0.63 and 0.63, which match. One draw at the pooled rate would need sensitivity about 2 standard errors above and FPR about 3 below expectation at once. The baseline therefore probably uses per-screen hit rates, which is information a screen designer would not have before the screen. The expected values were computed by the reviewer from Table 3 counts and are not stored as results. Treat the random row as a per-screen-prevalence classifier, not a matched random-selection comparator.Virtual CRISPR: Can LLMs Predict CRISPR Screen Results? · Appendix A.2; Table 3; Table 1, row "Random Baseline"; Table 8, row "Random N/A N/A" Methods, coverage and source GPT-4o (direct prompting, mean over strategies) on the Virtual CRISPR Simple Benchmark pheno-20261022-protocol-song2025-simple-hit-classification Aggregation: Not reported Virtual CRISPR: Can LLMs Predict CRISPR Screen Results? · Table 1, row "GPT-4o", column "FPR"

Source checking is not independent reproduction. Release 2026-10-23-5347695500ed.

Use this configuration

How it works, versions and access

Additional methodological details, versions and access terms have not yet been extracted into a structured profile for this configuration. Consult the linked sources and evaluation records below.

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.

0 evidence rows matching the loaded filters

Claims, original sources and review scope · Release 2026-10-23-5347695500ed
Property and statement	Original source and location	Review and provenance

No evidence rows match these filters. Choose another scope or clear the search.

How to read the evidence

Sources and history

Release 2026-10-23-5347695500ed · Record review: source checked

1 source record and release history

Virtual CRISPR: Can LLMs Predict CRISPR Screen Results? · Original source · Proceedings of the 24th Workshop on Biomedical Language Processing (BioNLP 2025), pages 354-364, 1 August 2025; ACL Anthology 2025.bionlp-1.30

Download this release (gzip)

Technical metadata and extraction receipts

Stable ID: pheno-20261022-config-song2025-gpt-4o-prompting-mean

areas: cells-tissues
contexts: research
method types: foundation_model
reported name: GPT-4o
configuration type: paper_evaluation
foundation model eligible: true
version: gpt-4o-2024-11-20
model version: gpt-4o-2024-11-20
parameters: Direct prompting; up to 10 combinations of chain-of-thought and 0-, 1- or 2-shot examples
temperature note: Temperature 0 with a fixed random seed
access reuse: OpenAI API
source locator: Table 1, row "GPT-4o"; Appendix A.1 and Table 7
provenance note: Prompted by Song et al. 2025; the model is third-party.

Related records

configuration of: GPT-4o
system: GPT-4o (prompting) on the Virtual CRISPR Difficult Benchmark
system: GPT-4o (direct prompting, mean over strategies) on the Virtual CRISPR Simple Benchmark

Suggest a correction