{"id":"W4211123163","doi":"10.1038/s43586-021-00093-4","title":"High-content CRISPR screening","year":2022,"lang":"en","type":"article","venue":"Nature Reviews Methods Primers","topic":"CRISPR and Genetic Engineering","field":"Biochemistry, Genetics and Molecular Biology","cited_by":574,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Guelph; University of Toronto","funders":"","keywords":"CRISPR; Computational biology; Trans-activating crRNA; Biology; Drug discovery; Multiplex; Genome editing; High-content screening; Gene; Genetics; Bioinformatics; Cell","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001795036,0.0001985105,0.0003129244,0.00005864822,0.0001572638,0.00001279049,0.0003153778,0.0001486837,0.0001275719],"category_scores_gemma":[0.0003351839,0.0001833758,0.0002439572,0.0001952226,0.00002293212,0.000002186022,0.0002972689,0.0006034257,0.000004184749],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002335376,"about_ca_system_score_gemma":0.00002928426,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001195335,"about_ca_topic_score_gemma":0.000001256822,"domain_scores_codex":[0.9982992,0.0005526431,0.0003046136,0.0004114994,0.0001638341,0.0002681424],"domain_scores_gemma":[0.999255,0.00002860153,0.0001087935,0.0004805154,0.00003623655,0.00009083778],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00002534049,0.00002114953,0.00006867613,0.00004009155,0.00005911829,0.000002261039,0.00002507575,0.000432907,0.5802688,0.0001688174,0.006425465,0.4124623],"study_design_scores_gemma":[0.0002057722,0.00009101119,0.0002954337,0.00001057347,0.00003994983,0.00002213423,0.00004984031,0.00003540063,0.1935475,0.00001966187,0.8054853,0.0001973846],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.005273668,0.3316712,0.6599932,0.0003244401,0.001362814,0.000619972,0.00001435263,0.00003430022,0.0007060036],"genre_scores_gemma":[0.02536811,0.007101409,0.9639415,0.001652391,0.0003813614,0.0001728022,0.0001128335,0.00005283695,0.001216776],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.7990599,"threshold_uncertainty_score":0.7477849,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03925068954497576,"score_gpt":0.4228328634316131,"score_spread":0.3835821738866373,"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."}}