{"id":"W1974966065","doi":"10.1016/j.neuron.2015.03.016","title":"Casting a Genome-wide Net for Learning Mutants","year":2015,"lang":"en","type":"letter","venue":"Neuron","topic":"Zebrafish Biomedical Research Applications","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of British Columbia Hospital; University of British Columbia","funders":"","keywords":"Mutant; Genome; Computational biology; Neuroscience; Biology; Computer science; Psychology; Genetics; 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":[0.002638668,0.0005699837,0.0007133431,0.0002814984,0.001445429,0.001990012,0.001192248,0.01644619,0.004179369],"category_scores_gemma":[0.009254283,0.0005312883,0.0006963686,0.000168691,0.00239133,0.001761815,0.001511516,0.0163269,0.002239916],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001879878,"about_ca_system_score_gemma":0.001007775,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002644237,"about_ca_topic_score_gemma":0.008185037,"domain_scores_codex":[0.9992633,0.0001553707,0.00004571367,0.0001146694,0.000315996,0.000104862],"domain_scores_gemma":[0.9971039,0.001944331,0.000125937,0.0001881525,0.000271441,0.0003661307],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0005780633,0.0001107987,0.001194992,0.0001005746,0.00009791699,0.001812884,0.0001442187,0.003107328,0.007363056,0.03726464,0.8660583,0.08216713],"study_design_scores_gemma":[0.0007359833,0.0003385878,0.001483275,0.0001580748,0.0001337625,0.001680091,0.000208315,0.01500857,0.01457483,0.1099686,0.8555773,0.0001326138],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"commentary","genre_gemma":"empirical","genre_scores_codex":[0.004862239,0.003887273,0.01528503,0.9452352,0.02013662,0.00004801038,0.0002749586,0.0005852054,0.00968549],"genre_scores_gemma":[0.1142209,0.006495796,0.02382039,0.7592133,0.02738679,0.0003509491,0.0002059033,0.0002889988,0.06801707],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01644619,"threshold_uncertainty_score":0.01398134,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.032970347774219,"score_gpt":0.3026044592812567,"score_spread":0.2696341115070377,"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."}}