{"id":"W4405359516","doi":"10.1016/j.jeem.2024.103104","title":"Storms, early education and human capital","year":2024,"lang":"en","type":"article","venue":"Journal of Environmental Economics and Management","topic":"Climate Change, Adaptation, Migration","field":"Social Sciences","cited_by":5,"is_retracted":false,"has_abstract":false,"ca_institutions":"Center for Interuniversity Research and Analysis on Organizations; Université de Sherbrooke","funders":"Social Sciences and Humanities Research Council; Social Sciences and Humanities Research Council of Canada; Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung","keywords":"Storm; Human capital; Economics; Natural resource economics; Capital (architecture); Geography; Economic growth; Meteorology; Archaeology","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0007743502,0.0001343553,0.0001727518,0.0006079844,0.0005601814,0.001452593,0.0003366824,0.000780218,0.0105748],"category_scores_gemma":[0.004466734,0.0001071212,0.0001677709,0.001313378,0.000908034,0.0009202102,0.0007069585,0.0006667055,0.0004245522],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001008233,"about_ca_system_score_gemma":0.001787537,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02794435,"about_ca_topic_score_gemma":0.06189607,"domain_scores_codex":[0.9997004,0.00008789428,0.00001631097,0.00001938029,0.00004038079,0.0001356163],"domain_scores_gemma":[0.9935037,0.0026149,0.001743215,0.0001832531,0.0002959109,0.001658907],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0003696496,0.0009987062,0.9282085,0.00009379029,0.0001531259,0.0005228898,0.001614571,0.002507553,0.0002349063,0.0222558,0.003216514,0.03982408],"study_design_scores_gemma":[0.00002861717,0.0002027653,0.9733157,0.00009477713,0.00007832506,0.0001408668,0.003409233,0.001148222,0.0001206221,0.01551142,0.005934217,0.00001519825],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9795374,0.002029961,0.0002434625,0.003305817,0.00004042996,0.000007987483,0.0003099094,0.000007727594,0.01451737],"genre_scores_gemma":[0.9972278,0.0005356912,0.000033919,0.00005517618,0.000020954,0.000001771852,0.00005059833,0.000001035949,0.002073003],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02794435,"threshold_uncertainty_score":0.05556339,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02614038874810211,"score_gpt":0.2585342554278664,"score_spread":0.2323938666797643,"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."}}