{"id":"W4415672655","doi":"10.2139/ssrn.5634611","title":"<p>Distributional Regression for Seasonal Data: An Application to River Flows</p>","year":2025,"lang":"","type":"preprint","venue":"SSRN Electronic Journal","topic":"Flood Risk Assessment and Management","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Seasonality; Flood myth; Regression; Regression analysis; Generalized additive model; Variation (astronomy); Distribution (mathematics); Complement (music); Linear regression","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":[0.003854344,0.001049801,0.001008517,0.001593955,0.0006027757,0.001017168,0.001710307,0.001364602,0.03128354],"category_scores_gemma":[0.02311218,0.001030256,0.001357978,0.00261359,0.0007967462,0.002427194,0.001750107,0.002683226,0.01496142],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006269523,"about_ca_system_score_gemma":0.0009970109,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01197755,"about_ca_topic_score_gemma":0.01310845,"domain_scores_codex":[0.998909,0.0005027917,0.00008871186,0.0002102227,0.0002282845,0.00006105759],"domain_scores_gemma":[0.993369,0.004696979,0.0001969628,0.001001714,0.0006008036,0.000134481],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001534645,0.0001235743,0.004703127,0.0002837469,0.0002654839,0.0004513622,0.0002154979,0.07057448,0.00408091,0.0684059,0.144369,0.7063734],"study_design_scores_gemma":[0.00005728846,0.00003910383,0.003238032,0.00003361109,0.00004262895,0.0001899292,0.00003928935,0.8916061,0.004520827,0.0729062,0.02728593,0.00004110258],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.004067651,0.0002273159,0.9818773,0.001025782,0.0001791883,0.00003954013,0.0008917574,0.009029045,0.002662365],"genre_scores_gemma":[0.1197101,0.0007057723,0.8375593,0.0003913571,0.000512974,0.0002732655,0.002802313,0.0124492,0.02559562],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.03128354,"threshold_uncertainty_score":0.1046538,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01256658441397841,"score_gpt":0.2905264688564511,"score_spread":0.2779598844424727,"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."}}