{"id":"W4254286547","doi":"10.1515/iupac.88.0966","title":"Keratin","year":2017,"lang":"en","type":"dataset","venue":"IUPAC Standards Online","topic":"Dyeing and Modifying Textile Fibers","field":"Engineering","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"],"consensus_categories":[],"category_scores_codex":[0.0002597504,0.0004220551,0.0005091572,0.0001442743,0.0001880117,0.0001943138,0.000616884,0.0003942644,0.000741753],"category_scores_gemma":[0.0002255387,0.0004201477,0.0001442935,0.00004734079,0.000107482,0.00007515559,0.00009206028,0.0007077885,0.00001293519],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00027457,"about_ca_system_score_gemma":0.0002145499,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006657444,"about_ca_topic_score_gemma":0.0003917591,"domain_scores_codex":[0.9984082,0.00001977634,0.0002908784,0.0003019698,0.0005762675,0.0004028894],"domain_scores_gemma":[0.9983556,0.00003112912,0.00009255157,0.001253944,0.0001174089,0.0001494082],"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":[0.000008543234,0.00002502807,5.815229e-7,0.0002403364,0.00008944765,0.00006472024,0.00001249011,0.0005362846,0.000003837131,0.000001532453,0.9942486,0.004768567],"study_design_scores_gemma":[0.0003381786,0.0000431718,0.000007467766,0.0003215917,0.0000741383,0.00001107546,0.000004809833,0.000558812,0.0000153557,0.00003554113,0.9981397,0.0004501483],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00006851706,0.0006264654,0.0002746392,0.00005295502,0.002276948,0.0001354705,0.9957446,0.0004276706,0.0003926807],"genre_scores_gemma":[0.00006733889,0.0006429731,0.0001522045,0.0000505464,0.0009048254,0.00001234755,0.997401,0.00007491328,0.0006938325],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.004318419,"threshold_uncertainty_score":0.9998251,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01637756376105941,"score_gpt":0.3884424311062843,"score_spread":0.3720648673452249,"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."}}