{"id":"W4229792522","doi":"10.1515/iupac.88.0814","title":"Fold","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); 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":[0.001856657,0.001790525,0.001536503,0.005029812,0.00112566,0.004244932,0.002724872,0.002149311,0.2806582],"category_scores_gemma":[0.01384183,0.0008235584,0.001886118,0.007698878,0.0004808682,0.004108234,0.003610674,0.001925161,0.3492818],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00206944,"about_ca_system_score_gemma":0.0034212,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01717485,"about_ca_topic_score_gemma":0.02883873,"domain_scores_codex":[0.9973783,0.0004479216,0.0005950261,0.0007413878,0.0005314444,0.0003058084],"domain_scores_gemma":[0.993486,0.001522025,0.0007058989,0.001626893,0.002262213,0.0003971363],"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":[0.00007088509,0.000009726266,0.0005513525,0.001126556,0.00001979496,0.00001265321,0.00002656376,0.00006948309,0.00008552011,0.0007797318,0.9924371,0.004810673],"study_design_scores_gemma":[0.00008724995,0.00001087866,0.00165885,0.0006030923,0.0000152654,0.00003760452,0.00006475479,0.00009534638,0.0001429421,0.001086577,0.9961789,0.00001857133],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00005958652,0.00007986381,0.0001035018,0.0001020845,0.00004012069,0.00002797512,0.9971038,0.000433329,0.002049693],"genre_scores_gemma":[0.0001945395,0.0001001594,0.0003465166,0.0001496522,0.00001343078,0.0001198915,0.9972428,0.0001593902,0.001673571],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.7193418,"threshold_uncertainty_score":0,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02586024909296726,"score_gpt":0.4710239122630733,"score_spread":0.445163663170106,"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."}}