{"id":"W2606573263","doi":"10.23889/ijpds.v1i1.269","title":"PATHS Data Resource: A population-based suite of linkable administrative records and metadata for population health research","year":2017,"lang":"en","type":"article","venue":"International Journal for Population Data Science","topic":"Food Security and Health in Diverse Populations","field":"Health Professions","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Manitoba; Manitoba Health","funders":"","keywords":"Gini coefficient; Operationalization; Population; Population health; Health equity; Metadata; Equity (law); Health care; Socioeconomic status; Social determinants of health; Geography; Business; Inequality; Environmental health; Medicine; Economic growth; Computer science; Political science; Economic inequality; Economics; World Wide Web","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":[0.01708117,0.0009978071,0.001412133,0.0156497,0.0017297,0.003438483,0.005570961,0.001324387,0.08061127],"category_scores_gemma":[0.1056486,0.001659016,0.001315888,0.02705563,0.0007896432,0.00436645,0.007009163,0.00226048,0.03618741],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.004937798,"about_ca_system_score_gemma":0.03409933,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.09986842,"about_ca_topic_score_gemma":0.1270151,"domain_scores_codex":[0.9901355,0.002667301,0.002819897,0.001365584,0.00263059,0.0003810365],"domain_scores_gemma":[0.9229318,0.02527419,0.007336224,0.01747816,0.02295031,0.004029238],"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":[0.0001814363,0.00007039813,0.01281884,0.002054623,0.0001665092,0.0001323927,0.000832707,0.001081885,0.0006059234,0.01490575,0.8989585,0.06819098],"study_design_scores_gemma":[0.000195209,0.00003023957,0.01237016,0.001262807,0.0001047131,0.0001116315,0.0006205197,0.00135962,0.0009837971,0.008493735,0.9743513,0.0001162027],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0005968876,0.0001653804,0.01213057,0.0007475654,0.0001442508,0.0009337289,0.9761028,0.004374941,0.004803911],"genre_scores_gemma":[0.007873452,0.00067541,0.08446556,0.0006241406,0.0001351024,0.005318533,0.8955255,0.001852574,0.003529631],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.09986842,"threshold_uncertainty_score":0.2696715,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.7370779347199065,"score_gpt":0.6597615478310586,"score_spread":0.0773163868888479,"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."}}