{"id":"W4235269352","doi":"10.1515/iupac.79.0852","title":"Artefact","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; 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":[],"consensus_categories":[],"category_scores_codex":[0.00321542,0.002916554,0.001699483,0.006303621,0.001295194,0.00493311,0.004009002,0.002608043,0.1949947],"category_scores_gemma":[0.02404695,0.001027107,0.003300393,0.007658207,0.0007331647,0.003403041,0.003694599,0.00222486,0.2200537],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001912146,"about_ca_system_score_gemma":0.004032045,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01760355,"about_ca_topic_score_gemma":0.03623715,"domain_scores_codex":[0.9963794,0.0008330711,0.0007046622,0.00109675,0.0006810471,0.0003049894],"domain_scores_gemma":[0.9893058,0.004194519,0.0007159156,0.003405412,0.001956772,0.000421632],"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":[0.0001288483,0.00001756883,0.0007641343,0.001308055,0.00006342298,0.00002724089,0.00003730924,0.0003567315,0.00007169918,0.001059316,0.9893328,0.006832987],"study_design_scores_gemma":[0.0001692324,0.00001620639,0.00122731,0.0005015161,0.00004364123,0.00005935921,0.0000617088,0.0003384372,0.0001707897,0.00272739,0.9946536,0.00003078132],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00008530521,0.00009497009,0.0003215525,0.00009242308,0.00005628913,0.00004518108,0.9967734,0.00122594,0.001304851],"genre_scores_gemma":[0.0003430187,0.00008222966,0.0008978867,0.000114485,0.00001275127,0.0002250586,0.9970832,0.0002501225,0.0009911392],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.1949947,"threshold_uncertainty_score":0,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01758491133715803,"score_gpt":0.4269380046441678,"score_spread":0.4093530933070098,"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."}}