{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002232856,0.0000940122,0.0001866037,0.0002451765,0.0002337014,0.0003152035,0.000170255,0.00004878688,0.00006054675],"category_scores_gemma":[0.0001381227,0.00008442031,0.0001397555,0.0001880914,0.0001731105,0.0005249688,0.00005028973,0.0002050364,0.00001045876],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001133454,"about_ca_system_score_gemma":0.0001259044,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0010114,"about_ca_topic_score_gemma":0.001291468,"domain_scores_codex":[0.9988444,0.00008716045,0.0003795481,0.0001332766,0.0003080084,0.0002475566],"domain_scores_gemma":[0.999388,0.0001443713,0.0002090035,0.00006667052,0.00007584407,0.0001160841],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00001780791,0.00004245658,0.7046959,0.0002640462,0.0004852413,0.000504275,0.008899149,0.0001623879,0.00003517226,0.2403237,0.009559936,0.03500989],"study_design_scores_gemma":[0.001344718,0.0005265959,0.5352179,0.001715017,0.0009007694,0.0004311597,0.01350979,0.01242617,0.0002564914,0.1776861,0.2547151,0.001270322],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9378484,0.001326783,0.000253421,0.001014913,0.001675771,0.0000938778,0.000003810803,0.00003245745,0.05775056],"genre_scores_gemma":[0.9971707,0.0006362443,0.0007243797,0.00004476302,0.001182415,0.000001133649,2.35925e-7,0.00001214168,0.000227959],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2451551,"threshold_uncertainty_score":0.344256,"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."}}