{"id":"W2169427943","doi":"10.1186/1476-072x-9-50","title":"Small-scale health-related indicator acquisition using secondary data spatial interpolation","year":2010,"lang":"en","type":"article","venue":"International Journal of Health Geographics","topic":"Health disparities and outcomes","field":"Social Sciences","cited_by":19,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada; Mitacs","keywords":"Neighbourhood (mathematics); Kriging; Spatial analysis; Multivariate interpolation; Statistics; Scale (ratio); Health geography; Computer science; Community health; Econometrics; Geography; Data mining; Mathematics; Public health; Cartography; Medicine; Health policy","routes":{"ca_aff":true,"ca_fund":true,"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":[0.002582263,0.0008772873,0.0007222701,0.002492615,0.0006664254,0.001057481,0.001311607,0.0002790832,0.003288948],"category_scores_gemma":[0.01100679,0.0003264486,0.001043172,0.005115282,0.0005043496,0.0005354186,0.001500961,0.0005672903,0.001007415],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001546391,"about_ca_system_score_gemma":0.005435234,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.1718592,"about_ca_topic_score_gemma":0.141931,"domain_scores_codex":[0.9984393,0.000464391,0.0001237746,0.0002789711,0.000558933,0.0001346366],"domain_scores_gemma":[0.9958198,0.001119426,0.0003257491,0.0006982136,0.001939352,0.00009746244],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0006630764,0.0003090569,0.1472993,0.00154247,0.0002498011,0.0004523069,0.002458449,0.2235899,0.01713712,0.01606196,0.009445919,0.5807905],"study_design_scores_gemma":[0.00007191925,0.000295684,0.129987,0.0002404154,0.0001547357,0.0002214054,0.00107581,0.8023051,0.02691523,0.01090789,0.02766094,0.0001638303],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.09110068,0.0001361697,0.896467,0.00008588306,0.0000443241,0.001007681,0.003796817,0.001902431,0.005458966],"genre_scores_gemma":[0.4516129,0.0003230137,0.5359098,0.00003854116,0.00001868445,0.001022027,0.008373505,0.0002181128,0.002483542],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.1718592,"threshold_uncertainty_score":0.3417177,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05861314591104202,"score_gpt":0.3874047294970631,"score_spread":0.328791583586021,"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."}}