{"id":"W6923039367","doi":"10.1371/journal.pone.0012797.g001","title":"Distribution of hip score from all UK registered Labrador Retrievers.","year":2015,"lang":"en","type":"other","venue":"Figshare","topic":"History of Computing Technologies","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Distribution (mathematics); Age groups; Population; Statistical analysis","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.00006232805,0.0002650653,0.000384378,0.0001457597,0.00002624206,0.00004949837,0.002509669,0.0005326189,0.01935318],"category_scores_gemma":[0.001333915,0.0002735384,0.00008801685,0.0003388045,0.00003274751,0.00007423714,0.0008387284,0.0002904358,0.001508407],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001772265,"about_ca_system_score_gemma":0.0002082281,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001529537,"about_ca_topic_score_gemma":0.00004976856,"domain_scores_codex":[0.9984735,0.00004404479,0.0002487308,0.0005474405,0.0004406244,0.0002456345],"domain_scores_gemma":[0.9974334,0.00006856425,0.000599846,0.001677304,0.0001431288,0.00007781311],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.000002034238,0.00001082924,0.000002116946,0.000076269,0.00002384683,0.00002144119,0.00005664724,6.690639e-7,0.000003806462,0.00008678369,0.997077,0.002638526],"study_design_scores_gemma":[0.0001780959,0.00004431445,0.0000887666,0.003245052,0.000006464443,0.000002539376,0.000003413831,0.0001484233,0.0001499868,0.0004612972,0.9953953,0.0002763605],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00003003737,0.005913347,0.001949038,0.0005510307,0.0009513762,0.0007823459,0.8172814,0.005193464,0.1673479],"genre_scores_gemma":[0.006123088,0.00001477711,0.02242524,0.0002733706,0.0007137378,0.0000769321,0.6434069,0.0004784052,0.3264875],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.1738745,"threshold_uncertainty_score":0.9999717,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08649259774773106,"score_gpt":0.2621917128171187,"score_spread":0.1756991150693877,"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."}}