{"id":"W3013218212","doi":"10.23889/ijpds.v4i2.1133","title":"Population Data BC: Supporting population data science in British Columbia.","year":2019,"lang":"en","type":"article","venue":"PubMed","topic":"Data Quality and Management","field":"Decision Sciences","cited_by":28,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia; The Quebec Population Health Research Network","funders":"","keywords":"Data access; Computer science; Variety (cybernetics); Data science; Data governance; Data quality; Identifier; Population; Linkage (software); Linked data; Record linkage; Data management; Process (computing); Database; World Wide Web; Business; Service (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":["open_science"],"consensus_categories":[],"category_scores_codex":[0.01020786,0.001002546,0.001114989,0.007739485,0.004865251,0.007173006,0.004170171,0.001866187,0.1288425],"category_scores_gemma":[0.05756055,0.001140531,0.0005880545,0.01695441,0.001676007,0.003064901,0.005936782,0.003941927,0.04099036],"about_ca_system_candidate":true,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.02986663,"about_ca_system_score_gemma":0.1457987,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9405252,"about_ca_topic_score_gemma":0.9242529,"domain_scores_codex":[0.992603,0.001280239,0.0007372224,0.0009498937,0.003768647,0.0006610972],"domain_scores_gemma":[0.9379489,0.01052003,0.001717047,0.005779956,0.03702592,0.007008103],"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":[0.00005379331,0.00002423475,0.004962574,0.0004687577,0.00003216642,0.0001214564,0.0003855183,0.0003428991,0.0002232573,0.005970839,0.9118181,0.07559635],"study_design_scores_gemma":[0.00003170843,0.00000593508,0.01558124,0.001165338,0.00001955299,0.00004476333,0.0003588766,0.0008471488,0.0002212956,0.003483128,0.978196,0.00004504644],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"methods","genre_scores_codex":[0.003632356,0.004528983,0.01556785,0.02958319,0.001717898,0.001289038,0.6600583,0.009765438,0.273857],"genre_scores_gemma":[0.05373865,0.01005273,0.06720372,0.01339894,0.0005414177,0.005124398,0.5607002,0.005811085,0.2834289],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9958298,"threshold_uncertainty_score":0.431021,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2812092852371275,"score_gpt":0.4112615858905286,"score_spread":0.1300523006534012,"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."}}