{"id":"W3163219775","doi":"10.11575/prism/25096","title":"Bayesian Calibration for Logit Model Microsimulations: Case for PECAS SD in San Diego","year":2017,"lang":"en","type":"dissertation","venue":"PRISM (University of Calgary)","topic":"demographic modeling and climate adaptation","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Calibration; Bayesian probability; Logit; Logistic regression; Statistics; Econometrics; Computer science; Geography; Mathematics","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01025649,0.0005150965,0.0007719329,0.001236453,0.0008424051,0.00214661,0.002165728,0.001169832,0.004683667],"category_scores_gemma":[0.04406947,0.0004472561,0.0008849162,0.001890295,0.0009580869,0.001904135,0.001903698,0.002151693,0.0004937082],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.004261364,"about_ca_system_score_gemma":0.001947959,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.09822174,"about_ca_topic_score_gemma":0.06758221,"domain_scores_codex":[0.9955038,0.00307141,0.000124707,0.000668506,0.0004056547,0.0002258579],"domain_scores_gemma":[0.9866987,0.009323957,0.0008632017,0.001260986,0.001595596,0.0002574928],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002065838,0.0001411881,0.04702909,0.0000896817,0.0001614754,0.0003221768,0.0007284697,0.7867854,0.000223329,0.1200401,0.005565841,0.03870666],"study_design_scores_gemma":[0.00003582629,0.00005513232,0.006935257,0.00003701119,0.0000276949,0.000064846,0.0004576383,0.9400927,0.0002654318,0.04673671,0.00525404,0.00003771577],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6459841,0.0006225252,0.308329,0.003636945,0.0001112884,0.0003333293,0.001523812,0.001052979,0.03840597],"genre_scores_gemma":[0.9591667,0.0002137924,0.03619003,0.0001453023,0.00001936977,0.0001725135,0.0008498122,0.0001087639,0.003133729],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.09822174,"threshold_uncertainty_score":0.1953,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08624720970741231,"score_gpt":0.3397859056105891,"score_spread":0.2535386959031768,"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."}}