{"id":"W4246569346","doi":"10.1515/iupac.88.0999","title":"Lymph","year":2017,"lang":"en","type":"dataset","venue":"IUPAC Standards Online","topic":"Hematological disorders and diagnostics","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Canada Research Chairs; University of Toronto","funders":"","keywords":"Glossary; Terminology; Computer science; Linguistics; Philosophy","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":[0.0002457798,0.0003839161,0.001021099,0.0001153484,0.0001633429,0.00007018916,0.0003287115,0.0006262647,0.004041288],"category_scores_gemma":[0.006026074,0.0002851037,0.0002531738,0.00006562347,0.0002256442,0.00003493858,0.0001691631,0.0005033367,0.00002177111],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001569035,"about_ca_system_score_gemma":0.001090733,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001220259,"about_ca_topic_score_gemma":0.0001405948,"domain_scores_codex":[0.9978231,0.00002872143,0.0003921008,0.0004404151,0.0009079227,0.0004076969],"domain_scores_gemma":[0.9975789,0.0001362017,0.0002379718,0.001286153,0.0004188873,0.0003419292],"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":[0.0002166445,0.0006195412,0.000199491,0.000422986,0.00009220152,0.001058073,0.000002315093,1.897064e-7,4.609147e-7,0.00001511758,0.9956522,0.001720794],"study_design_scores_gemma":[0.001735559,0.0003761242,0.0008127135,0.0007134864,0.0003779594,0.0001112962,0.00001329668,0.000005148762,0.000002638932,0.0002395856,0.9953087,0.0003034743],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0002016341,0.001894008,0.00003673379,0.0016192,0.0004988502,0.0003335073,0.9947459,0.00006616215,0.0006040058],"genre_scores_gemma":[0.00006105871,0.002722531,0.0001012325,0.001581526,0.0005919096,0.00001309352,0.9942943,0.00002999652,0.0006043576],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.005780294,"threshold_uncertainty_score":0.9999601,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02716282557288043,"score_gpt":0.4604289981478772,"score_spread":0.4332661725749968,"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."}}