{"id":"W4214653328","doi":"10.2139/ssrn.3990384","title":"A Spatio-Temporal Analysis of OECD Member Countries’ Health Care Systems: Effects of Imputation and Geographically and Temporally Weighted Regression on Inference","year":2021,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Spatial and Panel Data Analysis","field":"Economics, Econometrics and Finance","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Western University","funders":"","keywords":"Inference; Geographically Weighted Regression; Imputation (statistics); Regression; Econometrics; Statistics; Regression analysis; Geography; Computer science; Cartography; Data mining; Mathematics; Artificial intelligence; Missing data","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008383211,0.0001444205,0.0007450609,0.0006508027,0.0001172108,0.00005554163,0.00009290746,0.00009099075,0.00002822367],"category_scores_gemma":[0.00008146063,0.0001306603,0.0001417192,0.0006476113,0.00005582031,0.0001466336,0.00003413411,0.0003617221,0.000001524925],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001625174,"about_ca_system_score_gemma":0.0004752738,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001955268,"about_ca_topic_score_gemma":0.004025427,"domain_scores_codex":[0.9982562,0.00009559944,0.0007665461,0.0002874183,0.0001114018,0.0004828043],"domain_scores_gemma":[0.9983947,0.000141116,0.001013557,0.0001865586,0.0001765113,0.0000876155],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.000117075,0.00007930592,0.8829845,0.0003158691,0.00219922,0.000006683193,0.0007868067,0.0002079834,0.0000182664,0.1095148,0.0000200538,0.003749507],"study_design_scores_gemma":[0.008547906,0.006405951,0.7753603,0.001399438,0.003506183,0.0001686292,0.007207499,0.0540705,0.0006027712,0.136922,0.003929641,0.001879246],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9557804,0.03719128,0.00631786,0.0003202178,0.00007278701,0.000114609,0.000131572,0.000006442534,0.00006475559],"genre_scores_gemma":[0.9835112,0.01601416,0.000121934,0.00005170624,0.00002788265,0.000003487575,0.0002248447,0.000009024867,0.00003577042],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1076242,"threshold_uncertainty_score":0.5328173,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008999914041478888,"score_gpt":0.2439696408305103,"score_spread":0.2349697267890314,"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."}}