{"id":"W4245350732","doi":"10.1515/iupac.79.1375","title":"Hazardous Production Factor","year":2016,"lang":"en","type":"dataset","venue":"IUPAC Standards Online","topic":"Risk and Safety Analysis","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Glossary; Chemical nomenclature; Hazardous waste; Hazard; Computer science; Toxicology; Chemistry; Engineering; Biology; Philosophy; 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":[0.001302664,0.001658761,0.001496579,0.005401036,0.0005995373,0.00244094,0.001928653,0.001206097,0.116715],"category_scores_gemma":[0.01159255,0.0005659509,0.002147754,0.008205311,0.0002966187,0.002052005,0.001294184,0.001768054,0.09487025],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001792664,"about_ca_system_score_gemma":0.00268682,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02460382,"about_ca_topic_score_gemma":0.03116082,"domain_scores_codex":[0.9983711,0.0002333523,0.000337364,0.0004758253,0.00045788,0.0001245386],"domain_scores_gemma":[0.9951214,0.001729201,0.0006079383,0.0008307626,0.00150123,0.0002094208],"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":[0.0001219861,0.00002737226,0.003275088,0.00251908,0.0001009416,0.00002935223,0.0000261972,0.001027039,0.0001244623,0.001403986,0.9771165,0.01422803],"study_design_scores_gemma":[0.0001486698,0.00001713986,0.005480116,0.0006565996,0.00006024909,0.00006171333,0.00005473503,0.000386493,0.0001781008,0.002049625,0.9908798,0.00002681072],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0001162563,0.0001234989,0.00008977427,0.00003864319,0.00002406126,0.00001320118,0.9982958,0.0001298686,0.001168824],"genre_scores_gemma":[0.0009087005,0.0002495897,0.000496091,0.0000848596,0.00001507817,0.00008519652,0.9968581,0.00006001537,0.001242354],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.116715,"threshold_uncertainty_score":0.3904504,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05977542890376032,"score_gpt":0.4871958183503774,"score_spread":0.4274203894466171,"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."}}