{"id":"W4241632110","doi":"10.23889/ijpds.v4i2.1133","title":"Population Data BC: Supporting population data science in British Columbia","year":2020,"lang":"en","type":"article","venue":"International Journal for Population Data Science","topic":"Data Quality and Management","field":"Decision Sciences","cited_by":30,"is_retracted":false,"has_abstract":true,"ca_institutions":"The Quebec Population Health Research Network; University of British Columbia","funders":"","keywords":"Computer science; Data access; Linkage (software); Variety (cybernetics); Data quality; Identifier; Data science; Data management; Linked data; Record linkage; Population; Database; World Wide Web; Service (business); Business","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":[0.01821289,0.0008697411,0.001078352,0.01089856,0.005817066,0.009625025,0.004517056,0.001633367,0.06549624],"category_scores_gemma":[0.08586089,0.00114985,0.0006682751,0.02099653,0.002043222,0.004236911,0.007882499,0.003864265,0.01844755],"about_ca_system_candidate":true,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.04080372,"about_ca_system_score_gemma":0.1844499,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9522613,"about_ca_topic_score_gemma":0.9395707,"domain_scores_codex":[0.9870029,0.00330497,0.001262779,0.001388117,0.005990195,0.001051013],"domain_scores_gemma":[0.8880356,0.0241533,0.002666863,0.01206686,0.06430392,0.008773423],"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":[0.0001011325,0.00005987688,0.01519494,0.001003295,0.00008793319,0.0003119989,0.001441692,0.001996289,0.0006846778,0.0274862,0.7596665,0.1919654],"study_design_scores_gemma":[0.00004732706,0.000008126411,0.01949305,0.00170541,0.00003107392,0.00006912537,0.0007817095,0.003600285,0.0005054091,0.01261678,0.9610536,0.00008818667],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"empirical","genre_scores_codex":[0.01113146,0.007302576,0.07055425,0.05351398,0.001590674,0.002420526,0.491904,0.01906867,0.3425138],"genre_scores_gemma":[0.127944,0.01702772,0.2067878,0.01399359,0.0007170936,0.007004251,0.4461188,0.00894581,0.171461],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9591963,"threshold_uncertainty_score":0.2960531,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.4131892460550775,"score_gpt":0.5216265395244426,"score_spread":0.1084372934693651,"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."}}