{"id":"W3208991043","doi":"10.5281/zenodo.3738361","title":"Generation of Knockout Cell Lines Using CRISPR-Cas9 Technology","year":2020,"lang":"en","type":"article","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"CRISPR and Genetic Engineering","field":"Biochemistry, Genetics and Molecular Biology","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"CRISPR; Computer science; Genetics; Biology; Gene","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":[0.001683733,0.001085061,0.001692063,0.001856025,0.001303192,0.001326192,0.002203095,0.001419291,0.02088383],"category_scores_gemma":[0.0008453711,0.001229854,0.001160405,0.00123542,0.0006203537,0.0006697503,0.001322956,0.003713881,0.02065826],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005524665,"about_ca_system_score_gemma":0.001197022,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001467714,"about_ca_topic_score_gemma":0.003283387,"domain_scores_codex":[0.9983864,0.0001632036,0.0002107123,0.0003320684,0.0007018635,0.0002058359],"domain_scores_gemma":[0.9993593,0.0001212288,0.00007528243,0.0001800375,0.0001392649,0.0001248977],"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":[0.0002090821,0.0001591782,0.0003799002,0.0004038413,0.00004992261,0.0004055716,0.0001556905,0.0004100542,0.9699457,0.003163388,0.01287474,0.011843],"study_design_scores_gemma":[0.0001970193,0.0003106301,0.002639118,0.0001451361,0.0001484473,0.001690014,0.0001087687,0.002531385,0.6940687,0.001236046,0.2967973,0.0001275526],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.1464517,0.006387306,0.6277127,0.001344404,0.002715753,0.01124823,0.1252965,0.02199339,0.05685002],"genre_scores_gemma":[0.2328924,0.008672729,0.3590712,0.001492188,0.0002102456,0.01630349,0.1938151,0.007678708,0.179864],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.02088383,"threshold_uncertainty_score":0.06986338,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05297389630115775,"score_gpt":0.2940586908392702,"score_spread":0.2410847945381124,"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."}}