{"id":"W4210914372","doi":"10.1093/genetics/iyac003","title":"WormBase in 2022—data, processes, and tools for analyzing <i>Caenorhabditis elegans</i>","year":2022,"lang":"en","type":"article","venue":"Genetics","topic":"Genetics, Aging, and Longevity in Model Organisms","field":"Biochemistry, Genetics and Molecular Biology","cited_by":311,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ontario Institute for Cancer Research","funders":"U.S. National Library of Medicine; National Human Genome Research Institute; Medical Research Council","keywords":"Caenorhabditis elegans; Biology; Workflow; Genomics; Data science; Data curation; Genome; Alliance; Caenorhabditis; Computational biology; Genetics; Computer science; Database; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003685504,0.0001823199,0.0001798022,0.00007344244,0.0002104302,0.00009744133,0.0004801743,0.00008281079,0.00002684501],"category_scores_gemma":[0.000149641,0.0002202776,0.00003525144,0.0001870637,0.00006135078,0.00001046081,0.0006505173,0.0001446242,0.000001206292],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002135572,"about_ca_system_score_gemma":0.0001946868,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002279741,"about_ca_topic_score_gemma":0.0006291443,"domain_scores_codex":[0.9985374,0.00005853988,0.0002715428,0.0005970224,0.000168475,0.0003670816],"domain_scores_gemma":[0.9990745,0.00003585176,0.00008763386,0.0006390174,0.00007325064,0.00008969938],"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":[0.0003765032,0.000773993,0.05830402,0.0008453683,0.0002734166,0.00005702217,0.002328162,0.01472192,0.7948989,0.0001821392,0.04226876,0.08496977],"study_design_scores_gemma":[0.004044622,0.001760354,0.006772057,0.00003137364,0.0002062507,0.0001662293,0.001693206,0.004463657,0.1956193,0.001287451,0.7822797,0.001675763],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9724025,0.01098064,0.01485269,0.0003601641,0.0002223034,0.0005243716,0.0005682011,0.0000158878,0.00007323642],"genre_scores_gemma":[0.9882961,0.001480468,0.007598494,0.0006435922,0.0002562795,0.0001120946,0.001290339,0.00004945409,0.0002731582],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.740011,"threshold_uncertainty_score":0.898266,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02150787986185723,"score_gpt":0.2534754082379426,"score_spread":0.2319675283760854,"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."}}