{"id":"W4409184329","doi":"10.1007/s00362-025-01691-0","title":"Forecasting natural disaster frequencies using nonstationary count time series models","year":2025,"lang":"en","type":"article","venue":"Statistical Papers","topic":"Financial Risk and Volatility Modeling","field":"Economics, Econometrics and Finance","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"Institut Louis Bachelier; Natural Sciences and Engineering Research Council of Canada; Université d'Orléans","keywords":"Series (stratigraphy); Natural disaster; Time series; Computer science; Econometrics; Meteorology; Geography; Mathematics; Geology; Machine learning","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"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":[0.001701553,0.0005044728,0.0005360434,0.001241167,0.0003539638,0.001194183,0.001408164,0.0007857258,0.0008284615],"category_scores_gemma":[0.008328907,0.0003546192,0.0005375897,0.001015155,0.000436288,0.001297428,0.0006316241,0.001082786,0.0001382632],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001012299,"about_ca_system_score_gemma":0.0007432122,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.06388354,"about_ca_topic_score_gemma":0.0552622,"domain_scores_codex":[0.9996263,0.0001077479,0.0000271353,0.00009190597,0.00008830288,0.00005857129],"domain_scores_gemma":[0.9974129,0.001797016,0.0003804175,0.0001252346,0.0002010464,0.00008345083],"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":[0.00007850644,0.00005881593,0.02560662,0.00003489474,0.00006816589,0.00009894998,0.0001016129,0.9390975,0.0005745513,0.01180765,0.0007692395,0.02170341],"study_design_scores_gemma":[0.00000201619,0.000004226045,0.001315304,0.000001551961,0.000003361485,0.000003147468,0.000008721191,0.9971629,0.00005644531,0.001374113,0.00006541143,0.000002763379],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8389559,0.0005107779,0.1569053,0.0007724409,0.00005988306,0.00003917,0.0006706985,0.0002283049,0.00185747],"genre_scores_gemma":[0.9896027,0.0002917594,0.008701048,0.00002148929,0.00003236183,0.0000165274,0.0005150661,0.0000123451,0.0008067078],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.06388354,"threshold_uncertainty_score":0.1270234,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04153104364193647,"score_gpt":0.2435630750453514,"score_spread":0.2020320314034149,"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."}}