{"id":"W4406432091","doi":"10.1016/s0765-2046(97)80143-8","title":"10.1016/s0765-2046(97)80143-8","year":2000,"lang":"en","type":"article","venue":"Time to knit","topic":"Genetics, Bioinformatics, and Biomedical Research","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"","keywords":"Genomics; Gerontology; Functional genomics; Library science; Medicine; Biology; Genetics; Computer science; Genome; Gene","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0002438142,0.0001761331,0.0001579846,0.00006361557,0.00008525391,0.00005100955,0.0004054837,0.0001895244,0.9543101],"category_scores_gemma":[0.0001218806,0.0001564261,0.00009644512,0.00013771,0.0001238453,0.000003700077,0.0001458277,0.0001170642,0.975355],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000153117,"about_ca_system_score_gemma":0.0000728326,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001137937,"about_ca_topic_score_gemma":7.459074e-7,"domain_scores_codex":[0.9985967,0.0000386789,0.0002658172,0.0002845885,0.0003324941,0.0004816669],"domain_scores_gemma":[0.9990327,0.00001429608,0.00002971184,0.0004525204,0.00009711065,0.0003736248],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001155946,0.00005966236,4.324774e-7,0.00001497043,0.00003306574,0.000002346499,0.000009975911,0.00001119057,0.005184816,2.70541e-7,0.2566313,0.7379364],"study_design_scores_gemma":[0.0003463313,0.0006038981,0.00003314209,0.00001068188,0.00001088853,0.000007954934,0.000003469227,0.0001529437,0.009797242,0.000006204151,0.9888108,0.0002164181],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"other","genre_gemma":"other","genre_scores_codex":[0.00303675,0.0001829886,0.00001281423,0.0003348859,0.000004059421,0.0002263666,0.00003760758,0.00002370674,0.9961408],"genre_scores_gemma":[0.0007654044,0.00001438723,0.0004971443,0.0001657501,0.0003595512,0.00001846887,0.0001565209,0.00002297791,0.9979998],"genre_candidate":"other","genre_consensus":"other","teacher_disagreement_score":0.73772,"threshold_uncertainty_score":0.6378874,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008901069603388805,"score_gpt":0.221182971415853,"score_spread":0.2122819018124642,"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."}}