{"id":"W4240957902","doi":"10.17504/protocols.io.sfvebn6","title":"Cloning guides to lentiCRISPR v2 v2","year":2018,"lang":"fr","type":"preprint","venue":"","topic":"Radiomics and Machine Learning in Medical Imaging","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Cloning (programming); Computational biology; Biology; Computer science; Programming language","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":[],"consensus_categories":[],"category_scores_codex":[0.0008004603,0.001079325,0.0005160533,0.001638734,0.001155723,0.001622716,0.001669051,0.002226982,0.03766991],"category_scores_gemma":[0.0008711378,0.001247859,0.0009840558,0.0008316776,0.001147918,0.0008315924,0.001030664,0.002885997,0.02308032],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009101268,"about_ca_system_score_gemma":0.0009337229,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003697867,"about_ca_topic_score_gemma":0.007348494,"domain_scores_codex":[0.9990693,0.00008073119,0.0001038345,0.0002860199,0.0002933858,0.0001668188],"domain_scores_gemma":[0.9992532,0.000216486,0.0001190446,0.0001617002,0.00009800313,0.0001515734],"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":[0.0005679288,0.0002084022,0.0007240433,0.0002758236,0.00004752036,0.0008959178,0.0004396632,0.0006596892,0.928614,0.01602369,0.02305892,0.02848445],"study_design_scores_gemma":[0.0002510881,0.0001961593,0.003505644,0.0001049235,0.00008240947,0.001695969,0.0001857277,0.003976887,0.7612799,0.001818628,0.2268132,0.00008943366],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.3439519,0.003150758,0.4155635,0.003598175,0.003608192,0.002776824,0.06599741,0.02578126,0.1355718],"genre_scores_gemma":[0.45772,0.001443989,0.178962,0.002031642,0.0003229062,0.00132361,0.05164193,0.01719328,0.2893607],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.03766991,"threshold_uncertainty_score":0.1260183,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02072857487581218,"score_gpt":0.3469978174525049,"score_spread":0.3262692425766927,"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."}}