{"id":"W4249444026","doi":"10.1515/iupac.88.1183","title":"Pelvic Kidney","year":2017,"lang":"en","type":"dataset","venue":"IUPAC Standards Online","topic":"Renal and related cancers","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Canada Research Chairs; University of Toronto","funders":"","keywords":"Glossary; Terminology; Relation (database); Computer science; Linguistics; Philosophy; Data mining","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":[0.0007834306,0.000839268,0.001562167,0.002945027,0.000592285,0.002102884,0.00155613,0.001056207,0.1061054],"category_scores_gemma":[0.007765072,0.000452714,0.001921284,0.005407323,0.0002875131,0.001848602,0.001651987,0.001505877,0.05074511],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001118402,"about_ca_system_score_gemma":0.002725585,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01237083,"about_ca_topic_score_gemma":0.02102463,"domain_scores_codex":[0.9990753,0.0001214423,0.0002308064,0.0003027079,0.0001513543,0.0001183685],"domain_scores_gemma":[0.9975647,0.0006596986,0.0004573867,0.0006006223,0.0005736948,0.0001439428],"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":[0.0006044374,0.00002744187,0.01127111,0.007579602,0.0003440736,0.0002735795,0.00005660598,0.0005731036,0.0003004557,0.002557477,0.9174575,0.0589546],"study_design_scores_gemma":[0.0002799232,0.00003888037,0.02368182,0.004221051,0.0002903463,0.001536443,0.000115449,0.0003363076,0.0003655751,0.004345006,0.9647347,0.00005465476],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0009345196,0.002540879,0.000626593,0.0003029592,0.000126995,0.00008802821,0.9849475,0.0003816568,0.01005079],"genre_scores_gemma":[0.005349367,0.002646652,0.001646717,0.0007418267,0.00007993137,0.0002603143,0.98474,0.0001706086,0.004364608],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.1061054,"threshold_uncertainty_score":0.354958,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00819171544100034,"score_gpt":0.3881665429974505,"score_spread":0.3799748275564502,"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."}}