{"id":"W4248201755","doi":"10.1515/iupac.88.0694","title":"Diverticulum","year":2017,"lang":"en","type":"dataset","venue":"IUPAC Standards Online","topic":"Environmental Policies and Emissions","field":"Environmental Science","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; 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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0002149023,0.0003606648,0.000364677,0.00003000498,0.0005128856,0.00008258189,0.0008965614,0.0002774129,0.04900683],"category_scores_gemma":[0.0001953151,0.0002971501,0.0001631923,0.00004862133,0.0005298941,0.0001288988,0.001332199,0.000436688,0.0001658699],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005790895,"about_ca_system_score_gemma":0.00003786865,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002466864,"about_ca_topic_score_gemma":0.002033034,"domain_scores_codex":[0.9977345,0.00003575353,0.0002645217,0.0004745245,0.001018407,0.0004722342],"domain_scores_gemma":[0.9981961,0.00002241145,0.0002081299,0.001267769,0.000006930875,0.0002987009],"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":[0.00001301135,0.0001749203,0.0002212359,0.00001502397,0.00001926977,0.00003888571,0.00001244053,0.00002240637,0.00002833453,2.780271e-7,0.9979271,0.00152705],"study_design_scores_gemma":[0.0002583601,0.00008454581,0.00302989,0.00006689804,0.00007456042,0.000009806153,0.00002146335,0.000009757448,0.00001383409,0.00006589337,0.9959929,0.000372091],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0009332143,0.0001128944,0.000005693794,0.000522788,0.0004238612,0.0001764362,0.9967661,0.00002956212,0.001029397],"genre_scores_gemma":[0.00009657467,0.0006772468,0.00004671642,0.0005580911,0.0002939369,0.000008902716,0.9950333,0.0000255832,0.003259643],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.04884096,"threshold_uncertainty_score":0.9999481,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01194020590868049,"score_gpt":0.3870965604334316,"score_spread":0.3751563545247511,"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."}}