{"id":"W4200246977","doi":"10.1016/j.celrep.2021.110176","title":"Assessing kinetics and recruitment of DNA repair factors using high content screens","year":2021,"lang":"en","type":"article","venue":"Cell Reports","topic":"DNA Repair Mechanisms","field":"Biochemistry, Genetics and Molecular Biology","cited_by":26,"is_retracted":false,"has_abstract":true,"ca_institutions":"Beatrice Hunter Cancer Research Institute; Dalhousie University","funders":"National Institute of Environmental Health Sciences; National Institute of Diabetes and Digestive and Kidney Diseases; Natural Sciences and Engineering Research Council of Canada; Broad Institute; National Institute of General Medical Sciences; Simeon J. Fortin Charitable Foundation; Massachusetts General Hospital; National Cancer Institute; National Institutes of Health; Cancer Prevention and Research Institute of Texas","keywords":"Kinetics; DNA; Cell biology; Chemistry; Biology; Computational biology; Biochemistry; Physics","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001590648,0.0001374864,0.0001940952,0.00002414335,0.00003977575,0.00002533882,0.00003446575,0.0001238468,0.00001433702],"category_scores_gemma":[0.0001174794,0.000133603,0.0001050327,0.00005468544,0.00004853476,0.000004836006,0.0001560562,0.00004937651,1.575538e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000212099,"about_ca_system_score_gemma":0.00009606183,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003309883,"about_ca_topic_score_gemma":0.000007609972,"domain_scores_codex":[0.9988573,0.00005143276,0.0003636568,0.0004019286,0.0001532002,0.000172517],"domain_scores_gemma":[0.9990022,0.0000105799,0.0002594669,0.0004791128,0.0001620556,0.00008662818],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.000004050615,0.00007859099,0.00623354,0.00003899837,0.00003542365,0.0002615117,0.00002218137,0.00002024401,0.9929439,0.00001855668,0.00004564181,0.0002973966],"study_design_scores_gemma":[0.0001351726,0.00011312,0.004846928,0.00003469571,0.00005275404,0.0002067927,0.0003036581,0.00003831582,0.9915419,0.00004663361,0.002539889,0.0001401018],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9966953,0.0008337459,0.001246564,0.00001026774,0.0002811699,0.0001765109,0.000003727982,0.00001942692,0.0007332384],"genre_scores_gemma":[0.9871816,0.00006054479,0.01192854,0.00004088998,0.00004790222,0.000004201376,0.00007505644,0.00002344914,0.0006378338],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01068197,"threshold_uncertainty_score":0.5448171,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0837940059358756,"score_gpt":0.2955269682180997,"score_spread":0.2117329622822241,"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."}}