{"id":"W4235874517","doi":"10.1515/iupac.88.1257","title":"Proximal (in Anatomy)","year":2017,"lang":"en","type":"dataset","venue":"IUPAC Standards Online","topic":"History and advancements in chemistry","field":"Chemistry","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":[0.0009436472,0.001304002,0.001184437,0.003717049,0.0008739017,0.004178212,0.001667916,0.001200181,0.3535312],"category_scores_gemma":[0.01024384,0.0006609638,0.001242154,0.007194922,0.000474624,0.003304418,0.003189839,0.001627011,0.4089017],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001195444,"about_ca_system_score_gemma":0.002441705,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01055367,"about_ca_topic_score_gemma":0.01819006,"domain_scores_codex":[0.998566,0.000236876,0.0002693537,0.0004736443,0.0002680092,0.0001862374],"domain_scores_gemma":[0.996588,0.001063566,0.0003660448,0.00074288,0.0009947239,0.0002448513],"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":[0.00004613238,0.000005904083,0.000612847,0.0008480161,0.0000109608,0.00001285298,0.00003092504,0.00004618895,0.00007430517,0.0007486097,0.9905625,0.007000769],"study_design_scores_gemma":[0.00002553734,0.000004749003,0.001414773,0.0003134368,0.000007490822,0.00003324566,0.00004912528,0.00003156269,0.00007841623,0.00077759,0.9972555,0.000008638269],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.000127213,0.0002586823,0.0001898326,0.0001569935,0.0001070415,0.00002310719,0.9923056,0.0006296407,0.006201921],"genre_scores_gemma":[0.0007718241,0.0004510719,0.0007767275,0.0003746071,0.00004982563,0.0001418507,0.9901313,0.0004023751,0.006900413],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.3535312,"threshold_uncertainty_score":0.9221092,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01533173990482255,"score_gpt":0.4254021479845786,"score_spread":0.4100704080797561,"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."}}