{"id":"W4233297970","doi":"10.1515/iupac.76.0399","title":"Systemic Effect","year":2016,"lang":"en","type":"dataset","venue":"IUPAC Standards Online","topic":"History and advancements in chemistry","field":"Chemistry","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":[],"consensus_categories":[],"category_scores_codex":[0.0009692052,0.001397145,0.001301916,0.00264938,0.0006550355,0.002405245,0.001306521,0.001175989,0.170236],"category_scores_gemma":[0.007994345,0.0004135577,0.00192235,0.003517226,0.0002831967,0.001840137,0.001333257,0.001259084,0.09561262],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001127369,"about_ca_system_score_gemma":0.001857718,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008759379,"about_ca_topic_score_gemma":0.01710245,"domain_scores_codex":[0.9984975,0.0001945889,0.0003070937,0.0005490811,0.0003424986,0.0001093091],"domain_scores_gemma":[0.9964796,0.001381598,0.000580388,0.0006389503,0.0007324061,0.0001870032],"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":[0.000573178,0.00006329596,0.007232384,0.004845199,0.0001851976,0.0000878417,0.00006345927,0.0004320348,0.0005342861,0.001758767,0.9347083,0.04951594],"study_design_scores_gemma":[0.000165166,0.0000488514,0.01369022,0.0009629421,0.0001713334,0.0002142019,0.0000614064,0.0001876749,0.0004139231,0.002253407,0.9817886,0.00004222845],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0006540276,0.001394223,0.0004035354,0.0001694144,0.0001035079,0.00006511101,0.9884362,0.000553305,0.008220602],"genre_scores_gemma":[0.004046605,0.001541995,0.001543789,0.0005488837,0.00007373816,0.000311725,0.9824273,0.0002582869,0.009247675],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.170236,"threshold_uncertainty_score":0.5694962,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00810751509241067,"score_gpt":0.3736895818653442,"score_spread":0.3655820667729336,"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."}}