{"id":"W3158137528","doi":"10.1017/asb.2021.25","title":"GEOGRAPHIC RATEMAKING WITH SPATIAL EMBEDDINGS","year":2021,"lang":"en","type":"preprint","venue":"Astin Bulletin","topic":"Spatial and Panel Data Analysis","field":"Economics, Econometrics and Finance","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université Laval","funders":"","keywords":"Bivariate analysis; Smoothing; Construct (python library); Econometrics; Spatial analysis; Variance (accounting); Computer science; Interpolation (computer graphics); Geographic information system; Data mining; Geography; Cartography; Mathematics; Economics; Artificial intelligence; Machine learning; Remote sensing","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":["metaepi_narrow","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0004878753,0.0004017412,0.0009444459,0.0003703618,0.0001429685,0.000434018,0.0004963716,0.0003088113,0.01058006],"category_scores_gemma":[0.0001524514,0.0004287736,0.0003173508,0.0002827347,0.00008698874,0.00004179933,0.0006329599,0.0006860064,0.001039787],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000479197,"about_ca_system_score_gemma":0.00004245889,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.016604,"about_ca_topic_score_gemma":0.0006746924,"domain_scores_codex":[0.9975519,0.00003893955,0.0007853561,0.001112418,0.00009258372,0.000418803],"domain_scores_gemma":[0.9981333,0.00007821545,0.0007721799,0.0008081603,0.00009073193,0.0001173851],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0002768759,0.0005723416,0.9291012,0.001435972,0.00354007,0.0008429131,0.002097383,0.01587633,0.00003985349,0.01574943,0.016975,0.01349264],"study_design_scores_gemma":[0.00263518,0.0003519524,0.1915416,0.002093132,0.0006126577,0.00008940599,0.0006758557,0.01972022,0.0002683263,0.007095096,0.7697903,0.00512629],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7456462,0.01088394,0.1733533,0.007492472,0.002680657,0.0009922009,0.001809479,0.0003979944,0.05674377],"genre_scores_gemma":[0.9919289,0.000314338,0.004576293,0.0004292164,0.0004470818,0.00008364686,0.001034666,0.00006645505,0.001119353],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7528153,"threshold_uncertainty_score":0.9998164,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02439718544417614,"score_gpt":0.2035611988419549,"score_spread":0.1791640133977787,"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."}}