{"id":"W4243945590","doi":"10.1515/iupac.87.0128","title":"Cephalic","year":2016,"lang":"en","type":"dataset","venue":"IUPAC Standards Online","topic":"Names, Identity, and Discrimination Research","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Canada Research Chairs; University of Toronto","funders":"","keywords":"Glossary; Chemical nomenclature; Relation (database); Computer science; Psychology; Chemistry; Linguistics; Philosophy; Data mining; Organic chemistry","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":[0.0005807868,0.001269296,0.001131255,0.003294094,0.0008097396,0.003373727,0.001589435,0.001175838,0.2858829],"category_scores_gemma":[0.006321729,0.0004438084,0.001027072,0.006242158,0.000321899,0.002059922,0.002179781,0.001416065,0.3495791],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001215109,"about_ca_system_score_gemma":0.002135258,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01428393,"about_ca_topic_score_gemma":0.02553495,"domain_scores_codex":[0.9990079,0.0001460431,0.0001636488,0.000315927,0.0002179885,0.0001485613],"domain_scores_gemma":[0.9978445,0.0004860568,0.0002981352,0.0004926648,0.0006750886,0.000203606],"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":[0.00008795132,0.00001120205,0.001307498,0.0007298307,0.00001619556,0.00002562895,0.00002442052,0.00008223012,0.0000774894,0.0009480398,0.9886729,0.008016612],"study_design_scores_gemma":[0.00005015284,0.000007972372,0.002494786,0.000305988,0.000009462453,0.00005404374,0.00006757871,0.00008441155,0.0001071752,0.0008351764,0.9959734,0.000009890705],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0001881621,0.0001517666,0.0001054851,0.0001152838,0.00005807168,0.00001965674,0.9931358,0.0004195737,0.005806194],"genre_scores_gemma":[0.0007310602,0.0002192022,0.000334705,0.0002020907,0.00002950736,0.00006724083,0.9925079,0.0001599182,0.005748326],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.2858829,"threshold_uncertainty_score":0,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03821813732973404,"score_gpt":0.5052761823148031,"score_spread":0.467058044985069,"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."}}