{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.001197591,0.0009408533,0.00116568,0.0005897262,0.0001565243,0.000117048,0.001234609,0.0007599653,0.02185288],"category_scores_gemma":[0.001756351,0.000698692,0.0003613835,0.0004405522,0.0003604699,0.0001806146,0.0004544667,0.000906734,0.0005546622],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001587323,"about_ca_system_score_gemma":0.001948977,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001703483,"about_ca_topic_score_gemma":0.001738279,"domain_scores_codex":[0.9941049,0.000209852,0.0007886545,0.001032333,0.002855564,0.001008692],"domain_scores_gemma":[0.995372,0.000179808,0.0005862307,0.002441441,0.0009963467,0.0004241622],"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":[0.0003235329,0.0003026367,0.000003294404,0.0001110797,0.0002135566,0.000232518,0.00000413671,4.280514e-7,0.00004349993,0.000009228808,0.9965609,0.002195167],"study_design_scores_gemma":[0.001660681,0.0002051354,0.00001656918,0.0005960338,0.0002620005,0.00004770948,0.000008125632,0.000001027365,0.00003136724,0.0002503934,0.9959809,0.0009401243],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00002636945,0.001038911,0.00003228646,0.0003872907,0.001426623,0.0004701567,0.9960268,0.0003824333,0.0002091926],"genre_scores_gemma":[0.000002359464,0.0005210643,0.00006330557,0.0002921616,0.002789532,0.00002806296,0.9946163,0.0002891326,0.001398094],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.02129822,"threshold_uncertainty_score":0.9995464,"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."}}