{"id":"W2888676651","doi":"10.23889/ijpds.v3i1.436","title":"A Metadata Manifesto: The Need for Global Health Metadata","year":2018,"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 Calgary","funders":"","keywords":"Metadata; Health data; Manifesto; Work (physics); Population health; Data quality; Health care; Process (computing); Quality (philosophy); Population; Global health; Public health; Computer science; Data science; Business; Environmental health; Medicine; World Wide Web; Political science; Nursing; Engineering","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":["sts"],"consensus_categories":[],"category_scores_codex":[0.01118795,0.0001165616,0.0001830322,0.000151559,0.004187389,0.0003978675,0.003232946,0.00006196646,0.00009414133],"category_scores_gemma":[0.00547205,0.00007604945,0.00004946447,0.0003610999,0.0002326655,0.005792502,0.0005575941,0.000308663,0.00003872032],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005894188,"about_ca_system_score_gemma":0.001863063,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006228592,"about_ca_topic_score_gemma":0.0007860892,"domain_scores_codex":[0.9966512,0.0001499981,0.001083153,0.0003287274,0.001223512,0.0005634196],"domain_scores_gemma":[0.9960544,0.0004967846,0.0009771386,0.0007064663,0.001458286,0.000307002],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0004809029,0.00006855594,0.01334031,0.0001172426,0.00009683951,8.510018e-7,0.001102669,0.00003689975,0.00002471645,0.4917945,0.3942943,0.09864223],"study_design_scores_gemma":[0.001400406,0.0002008642,0.04091932,0.0001862728,0.00003036736,0.00005558662,0.0008918931,0.06459828,0.000003553154,0.01714661,0.8744205,0.0001463398],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.007882951,0.0001232579,0.8821951,0.08312874,0.02004131,0.001894854,0.003779741,0.00007218155,0.0008819065],"genre_scores_gemma":[0.871388,0.0002135177,0.06700971,0.04118614,0.01126188,0.0001772574,0.005741312,0.0000289145,0.002993321],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.863505,"threshold_uncertainty_score":0.997109,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.5205488656453303,"score_gpt":0.6212911584990422,"score_spread":0.1007422928537118,"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."}}