{"id":"W4213120068","doi":"10.1101/2022.02.19.480892","title":"Reproducibility metrics for CRISPR screens","year":2022,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"CRISPR and Genetic Engineering","field":"Biochemistry, Genetics and Molecular Biology","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"CRISPR; Context (archaeology); Computer science; Computational biology; Pace; Biology; Data science; Genetics; Gene; Geography","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.03472444,0.002001727,0.00201274,0.009737603,0.001182046,0.003722421,0.00278986,0.002111012,0.002452805],"category_scores_gemma":[0.1703888,0.0006548854,0.002199667,0.00790743,0.002358934,0.003660093,0.00327131,0.002566685,0.001267441],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002319738,"about_ca_system_score_gemma":0.001472856,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00136388,"about_ca_topic_score_gemma":0.000950423,"domain_scores_codex":[0.9377802,0.02016706,0.009279486,0.007579354,0.02404721,0.001146693],"domain_scores_gemma":[0.6945847,0.1980584,0.03464983,0.03675731,0.03311003,0.002839842],"domain_codex":null,"domain_gemma":"reproducibility","domain_candidate":"reproducibility","domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001843008,0.000675106,0.1107149,0.006572841,0.003009173,0.001090598,0.001842039,0.3024589,0.09106345,0.09902091,0.03015535,0.3515537],"study_design_scores_gemma":[0.0001745646,0.002644089,0.06826489,0.001020532,0.001096416,0.003674054,0.0006724603,0.5456634,0.1357819,0.1808398,0.05921763,0.0009503269],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.06617413,0.006107868,0.898492,0.0007523317,0.0004672417,0.0006085901,0.009178655,0.0091585,0.009060635],"genre_scores_gemma":[0.5966148,0.00185337,0.3776095,0.0006957566,0.0004017182,0.002194027,0.01460569,0.003287221,0.002737918],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9652755,"threshold_uncertainty_score":0.1836426,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01792319253131047,"score_gpt":0.2836018699550658,"score_spread":0.2656786774237553,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}