{"id":"W4245018558","doi":"10.1515/iupac.79.0760","title":"Acute","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; Hazard; Toxicology; 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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00167993,0.001756134,0.001639648,0.003422873,0.001159374,0.004039364,0.00278247,0.001954413,0.2415334],"category_scores_gemma":[0.01590135,0.0005892386,0.002059767,0.006112284,0.0003832596,0.00291341,0.002282457,0.001945311,0.2409013],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002021806,"about_ca_system_score_gemma":0.003391761,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01981957,"about_ca_topic_score_gemma":0.03471776,"domain_scores_codex":[0.9971548,0.0004927923,0.0004924619,0.0009814808,0.0005613889,0.0003169591],"domain_scores_gemma":[0.9933588,0.001680704,0.0007313312,0.001530167,0.002322145,0.0003768338],"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":[0.0001425711,0.00002036528,0.001799839,0.0009839942,0.00004063657,0.00002000613,0.00002470614,0.0001152506,0.00005176487,0.0008465621,0.9879525,0.008001827],"study_design_scores_gemma":[0.000182219,0.00002241289,0.004421308,0.0007717961,0.00005107976,0.00007041226,0.0001119496,0.0001668388,0.0001428312,0.001900441,0.9921308,0.00002788581],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0001524596,0.0001560181,0.0001252548,0.0001416685,0.00006203976,0.00004300279,0.9962587,0.0002389075,0.002821957],"genre_scores_gemma":[0.0006727103,0.0001700427,0.0004675013,0.0003131501,0.00003229637,0.0002727345,0.9947103,0.00009636988,0.003264805],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.7584666,"threshold_uncertainty_score":0,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01468001975759597,"score_gpt":0.4308624982053531,"score_spread":0.4161824784477571,"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."}}