{"id":"W2890617612","doi":"10.23889/ijpds.v3i4.765","title":"Making Sense of a Hot Mess: Cleaning and Validating Messy Administrative Data to study Supportive Housing in Winnipeg, Manitoba","year":2018,"lang":"en","type":"article","venue":"International Journal for Population Data Science","topic":"Geriatric Care and Nursing Homes","field":"Health Professions","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Manitoba; Manitoba Health","funders":"","keywords":"Missing data; Medical record; Population; Data collection; Database; Geography; Computer science; Medicine; Statistics; Environmental health","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01434252,0.0007508275,0.0006093941,0.004274507,0.004602279,0.003274769,0.003732658,0.0004747402,0.002022548],"category_scores_gemma":[0.0504485,0.0008188597,0.0005283459,0.008341665,0.002256222,0.001070303,0.00410696,0.0007605753,0.0005122005],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.01930249,"about_ca_system_score_gemma":0.04313769,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9447915,"about_ca_topic_score_gemma":0.96966,"domain_scores_codex":[0.9885235,0.004937757,0.001127623,0.001279809,0.002886121,0.001245072],"domain_scores_gemma":[0.9724861,0.004919472,0.004579736,0.003834939,0.01266388,0.001515813],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001525068,0.0001641135,0.852715,0.0009031187,0.0002480719,0.0009727844,0.05317841,0.001536997,0.001965952,0.002698046,0.01737229,0.06809279],"study_design_scores_gemma":[0.00003296289,0.00007459412,0.8921552,0.0009007724,0.0001358323,0.0001712995,0.04576425,0.001324292,0.001299064,0.0004926861,0.05758508,0.00006398006],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9392968,0.002446448,0.01463109,0.005081924,0.0002051802,0.007233756,0.02212199,0.0001741065,0.00880867],"genre_scores_gemma":[0.9170071,0.001754627,0.05458858,0.002065306,0.00008314632,0.005991327,0.01349799,0.0001930435,0.004818895],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0552085,"threshold_uncertainty_score":0.14005,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.4253102651549672,"score_gpt":0.5743870164524415,"score_spread":0.1490767512974743,"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."}}