{"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":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.001956083,0.0002334951,0.0003689428,0.0003627394,0.0007107478,0.0002977293,0.0008710905,0.0003974545,0.02055042],"category_scores_gemma":[0.003019004,0.000191675,0.0001765399,0.0003551219,0.0006969345,0.0002196933,0.0001868073,0.0003789365,0.00002483807],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007623659,"about_ca_system_score_gemma":0.002897488,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.006891694,"about_ca_topic_score_gemma":0.05355757,"domain_scores_codex":[0.9952093,0.0003915559,0.0003357667,0.0004292848,0.003005691,0.0006283857],"domain_scores_gemma":[0.9977304,0.0001818193,0.0001732828,0.0005329523,0.001036663,0.0003448552],"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":[0.00002316401,0.0001465057,0.00004587162,0.00006814755,0.00002866349,0.00003459407,0.0001060689,3.550073e-8,9.442666e-7,0.0006882544,0.9944859,0.004371892],"study_design_scores_gemma":[0.000405769,0.00004750325,0.00006457599,0.00009721536,0.00004463802,7.403436e-7,0.0003707414,1.57632e-7,0.000001768285,0.001922751,0.996786,0.0002581521],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00004557351,0.0007056085,0.00004258953,0.003969247,0.001226016,0.0003223303,0.9896301,0.00007402932,0.003984481],"genre_scores_gemma":[0.00004837356,0.00474851,0.00001100315,0.0002505704,0.002412965,0.00001776339,0.9782147,0.00002007708,0.01427606],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.04666587,"threshold_uncertainty_score":0.9997215,"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."}}