{"id":"W4243737734","doi":"10.1515/iupac.79.1438","title":"Hypo–","year":2016,"lang":"en","type":"dataset","venue":"IUPAC Standards Online","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Glossary; Chemical nomenclature; Computer science; Toxicology; Library science; Chemistry; Philosophy; Biology; Linguistics; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00194349,0.002163019,0.001544497,0.00330789,0.000961364,0.003727911,0.003086511,0.001797797,0.2117389],"category_scores_gemma":[0.01653872,0.0006571937,0.002543497,0.005389212,0.0004288717,0.003052816,0.002491918,0.001812508,0.227824],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001464565,"about_ca_system_score_gemma":0.003239002,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01472457,"about_ca_topic_score_gemma":0.03165489,"domain_scores_codex":[0.9973737,0.0005103154,0.0004501808,0.0009529679,0.0004500566,0.0002628702],"domain_scores_gemma":[0.9941811,0.001702515,0.0005699907,0.001615613,0.001604067,0.0003266559],"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":[0.0001543883,0.00001809195,0.001166257,0.001164983,0.00005935867,0.00001606852,0.00002632634,0.0001615057,0.00008391689,0.0006863321,0.9905562,0.005906541],"study_design_scores_gemma":[0.0002137946,0.00002380048,0.002855122,0.0005121419,0.00005837772,0.00005694116,0.00008244913,0.0002602903,0.0001731214,0.002137048,0.9935975,0.00002941031],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0001354252,0.0001114953,0.0001579536,0.0001125714,0.00004938725,0.00003567984,0.9970196,0.0005647187,0.001813084],"genre_scores_gemma":[0.0005263524,0.0001071035,0.0005672072,0.0002031939,0.00001933633,0.0002113739,0.9963157,0.0001906612,0.001859084],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.7882611,"threshold_uncertainty_score":0,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01779095934293641,"score_gpt":0.424136144853112,"score_spread":0.4063451855101756,"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."}}