{"id":"W4391824537","doi":"10.1002/for.3086","title":"Space, mortality, and economic growth","year":2024,"lang":"en","type":"article","venue":"Journal of Forecasting","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"St. Francis Xavier University","funders":"","keywords":"Gross domestic product; Econometrics; Model selection; Space (punctuation); Economics; Economic model; Growth model; Selection (genetic algorithm); Lag; Computer science; Statistics; Mathematics; Macroeconomics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001112771,0.0003839744,0.0002513351,0.0009169417,0.0002291291,0.0008941708,0.0002999862,0.0004164566,0.002798291],"category_scores_gemma":[0.004767464,0.00009103528,0.0003744356,0.0008815205,0.0007292913,0.0008167338,0.0007544585,0.0006117944,0.0001740615],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008339091,"about_ca_system_score_gemma":0.0005995181,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009642974,"about_ca_topic_score_gemma":0.004859744,"domain_scores_codex":[0.9997427,0.0001351102,0.000008307697,0.00003747633,0.00003747122,0.00003892925],"domain_scores_gemma":[0.9981483,0.001228366,0.0002478355,0.00008850197,0.0001940482,0.0000929298],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.0001140143,0.0000627125,0.121148,0.00005018939,0.00009748284,0.0001685689,0.0001498351,0.790412,0.0003916147,0.06425687,0.001524272,0.02162448],"study_design_scores_gemma":[0.00000567904,0.00007733145,0.035344,0.00002660186,0.00002177055,0.00004683886,0.0002426431,0.9159191,0.0002457188,0.04644362,0.00161128,0.00001540563],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9505174,0.001573394,0.03724268,0.001558863,0.00007492589,0.00001443943,0.0004516531,0.00007768757,0.008489029],"genre_scores_gemma":[0.9984458,0.0002719884,0.0006326957,0.0000119874,0.00001368346,0.000004082608,0.00008508477,0.000003180145,0.000531446],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.009642974,"threshold_uncertainty_score":0.01917368,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05966678313283622,"score_gpt":0.3299953345094367,"score_spread":0.2703285513766004,"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."}}