{"id":"W4240904685","doi":"10.1515/iupac.79.1827","title":"Polyuria","year":2016,"lang":"en","type":"dataset","venue":"IUPAC Standards Online","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Glossary; Chemical nomenclature; Computer science; Toxicology; Library science; Chemistry; Philosophy; Biology; Linguistics","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":[0.0009560552,0.001236539,0.001559945,0.003547343,0.0008251569,0.002738804,0.001850225,0.001243906,0.1211313],"category_scores_gemma":[0.01107108,0.0004210442,0.001554608,0.006412121,0.0002938484,0.001643986,0.001497039,0.001324738,0.07384336],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001010607,"about_ca_system_score_gemma":0.002201122,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01596571,"about_ca_topic_score_gemma":0.02601624,"domain_scores_codex":[0.9983678,0.0002269666,0.0003275314,0.0006373684,0.0002896138,0.0001507756],"domain_scores_gemma":[0.9964372,0.00106766,0.00067268,0.0008036719,0.0007896509,0.000229098],"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":[0.0003377856,0.00003024977,0.006584622,0.002083767,0.0001434933,0.000067766,0.00003621998,0.0001762762,0.0001342112,0.0008992184,0.9709218,0.01858451],"study_design_scores_gemma":[0.0003305717,0.00004139559,0.02319016,0.001720162,0.0002064107,0.0004461631,0.0001107691,0.0003185389,0.0002360154,0.003029219,0.9703122,0.00005829514],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0004503742,0.0003925681,0.0001584562,0.0001002596,0.00005802654,0.00002880629,0.9966632,0.0002270371,0.001921324],"genre_scores_gemma":[0.002021591,0.0004689811,0.0006008159,0.0002685331,0.00004083057,0.0001895884,0.9937517,0.0001040443,0.002553952],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.1211313,"threshold_uncertainty_score":0,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01550409741733478,"score_gpt":0.4255827680781493,"score_spread":0.4100786706608145,"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."}}