{"id":"W2890003877","doi":"10.23889/ijpds.v3i4.754","title":"Provincial Data-linkage to Address Complex Policy Challenges","year":2018,"lang":"en","type":"article","venue":"International Journal for Population Data Science","topic":"Health disparities and outcomes","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Government of British Columbia","funders":"","keywords":"Government (linguistics); Analytics; Business; Public relations; Presentation (obstetrics); Public policy; Data science; Economics; Computer science; Political science; Economic growth","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":[0.04401801,0.0005348144,0.001153947,0.007612934,0.009514097,0.01674587,0.005683371,0.003316794,0.01932001],"category_scores_gemma":[0.1188932,0.0008551715,0.001136455,0.0249845,0.003648494,0.006902629,0.01539776,0.004697264,0.002860597],"about_ca_system_candidate":true,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.05819583,"about_ca_system_score_gemma":0.2063498,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.6683629,"about_ca_topic_score_gemma":0.6204404,"domain_scores_codex":[0.9598177,0.01708314,0.002498772,0.003848695,0.01217309,0.004578616],"domain_scores_gemma":[0.9197942,0.0274397,0.004708451,0.01342263,0.02937028,0.005264578],"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":[0.0001528799,0.0001602096,0.03604387,0.001664672,0.0002416416,0.0004271012,0.007180783,0.01775759,0.0005300755,0.4635724,0.2227493,0.2495195],"study_design_scores_gemma":[0.00007156163,0.00004757309,0.01458768,0.00192238,0.0001012662,0.0001851082,0.01003523,0.01773793,0.001315131,0.1661445,0.7877406,0.0001110459],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"commentary","genre_gemma":"empirical","genre_scores_codex":[0.04826479,0.008651122,0.1703495,0.3707699,0.002256393,0.002815015,0.04840445,0.003542978,0.3449458],"genre_scores_gemma":[0.6643313,0.008582494,0.2111699,0.0191359,0.0008169516,0.002182346,0.03018119,0.0009339555,0.0626659],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9418042,"threshold_uncertainty_score":0.6671804,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3449094083203832,"score_gpt":0.5399191026791207,"score_spread":0.1950096943587375,"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."}}