{"id":"W2991862006","doi":"10.23889/ijpds.v4i1.1124","title":"Concept Dictionary and Glossary at MCHP","year":2019,"lang":"en","type":"article","venue":"International Journal for Population Data Science","topic":"Medical Coding and Health Information","field":"Health Professions","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Manitoba; Manitoba Health","funders":"","keywords":"Glossary; Documentation; Computer science; Consistency (knowledge bases); Globe; Data science; Government (linguistics); Knowledge management; Terminology; World Wide Web; Artificial intelligence; Linguistics; Psychology","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.002212574,0.00006872524,0.0001009418,0.0001926443,0.001301166,0.00005653145,0.0007143626,0.00006400605,0.0006628837],"category_scores_gemma":[0.0009315244,0.00005654905,0.0000179245,0.0001248785,0.00009994467,0.002191358,0.0004440077,0.0002910752,0.0001577408],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003633734,"about_ca_system_score_gemma":0.0002999537,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008990662,"about_ca_topic_score_gemma":0.00001877675,"domain_scores_codex":[0.9980863,0.00005371139,0.0005500293,0.0002235081,0.0008281164,0.0002583023],"domain_scores_gemma":[0.9983858,0.0003089761,0.0003828865,0.0002390836,0.0004754981,0.0002077797],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0003762393,0.0000451584,0.8087056,0.0001208509,0.00003504683,0.000003508372,0.002225209,0.0002735404,0.0005584474,0.04301755,0.09028807,0.05435081],"study_design_scores_gemma":[0.001602713,0.00008917529,0.5263835,0.0002469075,0.000009160548,0.00008810816,0.0004970736,0.08854513,0.000009525862,0.002884122,0.3794905,0.0001540957],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9504256,0.0001638551,0.009459588,0.01257601,0.01966484,0.0009143917,0.0006621256,0.00007668578,0.00605692],"genre_scores_gemma":[0.9909001,0.0001067631,0.002260209,0.002715321,0.0008960082,0.000009743626,0.000749893,0.000006645661,0.0023553],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2892025,"threshold_uncertainty_score":0.999999,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3181216733610139,"score_gpt":0.5508663089012783,"score_spread":0.2327446355402644,"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."}}