{"id":"W4251329047","doi":"10.1515/iupac.88.1116","title":"Neuron","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); Medical terminology; Computer science; Linguistics; Philosophy; Data mining","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":[0.001240535,0.001409315,0.00132987,0.003668042,0.001141759,0.004241531,0.002788015,0.001851106,0.2613363],"category_scores_gemma":[0.01440279,0.0005957437,0.001616698,0.007306017,0.0004247613,0.003336958,0.002571146,0.001750399,0.2913253],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002066592,"about_ca_system_score_gemma":0.003787449,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02041634,"about_ca_topic_score_gemma":0.03849824,"domain_scores_codex":[0.997923,0.0003336913,0.0003979812,0.0006323284,0.0004736768,0.0002393794],"domain_scores_gemma":[0.9947399,0.001358944,0.0004138402,0.001297041,0.00187898,0.0003113494],"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":[0.00006265163,0.000008735114,0.0007050419,0.0008159325,0.00001833052,0.00001256757,0.00002193678,0.00008667695,0.00004370394,0.0009245775,0.990374,0.006925855],"study_design_scores_gemma":[0.00007544569,0.00001007256,0.001957325,0.0006581686,0.00001934194,0.00004446298,0.00007666135,0.0001067225,0.0001052731,0.001962819,0.9949673,0.00001647944],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0001075088,0.0001940433,0.0001388843,0.0002260437,0.0000789039,0.00002969435,0.9943105,0.0003576622,0.004556776],"genre_scores_gemma":[0.0004753226,0.000255224,0.0004379822,0.0002823513,0.00002156637,0.0001476919,0.9945656,0.0001278174,0.003686417],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.7386637,"threshold_uncertainty_score":0,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02520607191994563,"score_gpt":0.4581165074010968,"score_spread":0.4329104354811512,"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."}}