{"id":"W4229789893","doi":"10.1515/iupac.79.1521","title":"Knock-In","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; Multidisciplinary approach; Hazard; Toxicology; Library science; Chemistry; Philosophy; Biology; Linguistics; Political science; Law","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":[0.001216022,0.001822901,0.001694841,0.002842583,0.001502479,0.004749338,0.002349732,0.002050829,0.2420687],"category_scores_gemma":[0.01254757,0.0005658932,0.002170127,0.004524777,0.0004172694,0.003266375,0.002262317,0.00172448,0.2182992],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001067007,"about_ca_system_score_gemma":0.002283649,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0134045,"about_ca_topic_score_gemma":0.02325028,"domain_scores_codex":[0.9978215,0.0003062326,0.0003415347,0.0008879517,0.0003429087,0.0002999097],"domain_scores_gemma":[0.9958938,0.00145931,0.0004575847,0.001102963,0.0008137535,0.0002726339],"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":[0.0002058221,0.00002442941,0.004841803,0.001090095,0.00007010189,0.00008953589,0.0000326538,0.0001590632,0.0001058444,0.001516039,0.9806381,0.01122644],"study_design_scores_gemma":[0.0001557226,0.00002250855,0.006026543,0.0007562094,0.0001002304,0.0002870514,0.0001406464,0.0002688628,0.0002128917,0.002701931,0.9892901,0.00003720381],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0007014548,0.0007494288,0.0003411451,0.0003633486,0.0002636235,0.00004889957,0.9872449,0.0008729566,0.009414199],"genre_scores_gemma":[0.00319268,0.0006988063,0.0008522699,0.001094011,0.00009914637,0.0001942377,0.984839,0.0003907933,0.008639139],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.7579313,"threshold_uncertainty_score":0,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01598019048904965,"score_gpt":0.4282779126651634,"score_spread":0.4122977221761137,"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."}}