{"id":"W4241401731","doi":"10.1515/iupac.78.0285","title":"Exocon","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 Guelph","funders":"","keywords":"Glossary; Chemical nomenclature; Relation (database); Computer science; Management science; Data science; Chemistry; Engineering; Data mining; 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":[0.001157608,0.001957309,0.001373305,0.00546887,0.001191201,0.00366362,0.002709366,0.001962502,0.1659206],"category_scores_gemma":[0.009192582,0.0006205672,0.001348509,0.00902285,0.0004472978,0.003266932,0.002801602,0.001800086,0.2083077],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001941974,"about_ca_system_score_gemma":0.003485337,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02565954,"about_ca_topic_score_gemma":0.05160238,"domain_scores_codex":[0.9983889,0.0002615915,0.0002863064,0.000501567,0.0003550705,0.0002066456],"domain_scores_gemma":[0.9967085,0.0008681784,0.0004162055,0.000777658,0.0009614354,0.0002679208],"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":[0.00005262612,0.00001102896,0.000719141,0.0007913056,0.00001730203,0.00001984524,0.00003106295,0.0001152703,0.00008523542,0.0009859971,0.9937719,0.003399365],"study_design_scores_gemma":[0.00005737578,0.000007698943,0.001374522,0.0003977186,0.00001289119,0.00003752961,0.00006344521,0.0001051166,0.0001053401,0.001017543,0.9968065,0.00001432334],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0000577824,0.00006989751,0.00005263119,0.00006699871,0.00001781896,0.000008572973,0.9985038,0.0001779451,0.001044647],"genre_scores_gemma":[0.0001890596,0.00009026918,0.0001862701,0.00007651343,0.000006366497,0.00005115676,0.9983774,0.00006473366,0.0009581986],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.1659206,"threshold_uncertainty_score":0,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01829122166097484,"score_gpt":0.4292654264226888,"score_spread":0.410974204761714,"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."}}