{"id":"W4245109157","doi":"10.1515/iupac.76.0371","title":"Response","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; Toxicokinetics; Relation (database); Hazard; Computer science; Toxicology; Medicine; Chemistry; Pharmacology; Data mining; Biology; Linguistics; Philosophy","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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.002699946,0.001220296,0.001360033,0.002190561,0.000978653,0.002923009,0.001595997,0.002920986,0.3453307],"category_scores_gemma":[0.03537817,0.0005489994,0.001496245,0.003660066,0.0004115092,0.0027267,0.002690362,0.002214352,0.3070142],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001864322,"about_ca_system_score_gemma":0.002632004,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01187881,"about_ca_topic_score_gemma":0.0187263,"domain_scores_codex":[0.996117,0.000960056,0.0006178353,0.001093988,0.0007902091,0.0004208504],"domain_scores_gemma":[0.9874749,0.005172154,0.001152611,0.001971788,0.003588566,0.0006400511],"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":[0.00004363596,0.00000435815,0.0003129636,0.0002295553,0.000006324819,0.000006866245,0.000007614765,0.00001774827,0.00001340968,0.0001269527,0.9974342,0.001796417],"study_design_scores_gemma":[0.0001553433,0.00001466688,0.002714941,0.0005548395,0.00001888365,0.00004530757,0.0001230211,0.00006762903,0.00009623278,0.000801514,0.9953861,0.00002155068],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0001883526,0.0001564185,0.00009083753,0.001550934,0.0004532651,0.00006055997,0.9933205,0.0001996885,0.003979531],"genre_scores_gemma":[0.001445981,0.0002694767,0.0004196916,0.004667186,0.0002949381,0.0006432272,0.9769273,0.0002160506,0.01511617],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.6546693,"threshold_uncertainty_score":0,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01790468620047418,"score_gpt":0.4321551911440321,"score_spread":0.4142505049435579,"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."}}