{"id":"W4241169361","doi":"10.1515/iupac.88.0790","title":"Facies","year":2017,"lang":"en","type":"dataset","venue":"IUPAC Standards Online","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Canada Research Chairs; University of Toronto","funders":"","keywords":"Glossary; Terminology; Relation (database); Computer science; Linguistics; Data mining; 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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.001130634,0.001517127,0.001273536,0.004699886,0.001142328,0.004285039,0.002153453,0.00161248,0.3291641],"category_scores_gemma":[0.0141481,0.0006326845,0.001559902,0.009862579,0.0004062004,0.003577918,0.002926044,0.001694182,0.3632591],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001791226,"about_ca_system_score_gemma":0.003041397,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02390915,"about_ca_topic_score_gemma":0.03570969,"domain_scores_codex":[0.998041,0.0002921364,0.000397288,0.00053498,0.0004870981,0.0002476217],"domain_scores_gemma":[0.9946672,0.001444918,0.0005728519,0.001214059,0.001787274,0.0003137412],"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":[0.00005557983,0.000008206285,0.0007589975,0.0007541816,0.0000161598,0.00001383702,0.00002558673,0.0000629276,0.00003799842,0.0009379204,0.9911249,0.006203611],"study_design_scores_gemma":[0.00004514979,0.000006450454,0.001788134,0.0004486177,0.00001035164,0.00003875333,0.00006429895,0.0000530791,0.0000623013,0.0009172222,0.9965526,0.0000129795],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00009367707,0.0001380342,0.00009199372,0.0001447457,0.00005653845,0.00002026701,0.9947331,0.0003417724,0.004379844],"genre_scores_gemma":[0.0004447481,0.0002150788,0.0003063342,0.0002083032,0.00002810814,0.0000966321,0.9938002,0.0001848832,0.004715639],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.6708359,"threshold_uncertainty_score":0,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02880806798183036,"score_gpt":0.4713362258370005,"score_spread":0.4425281578551702,"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."}}