{"id":"W2751868120","doi":"10.1016/j.scitotenv.2017.08.276","title":"EMD-regression for modelling multi-scale relationships, and application to weather-related cardiovascular mortality","year":2017,"lang":"en","type":"article","venue":"The Science of The Total Environment","topic":"Climate Change and Health Impacts","field":"Environmental Science","cited_by":17,"is_retracted":false,"has_abstract":false,"ca_institutions":"Institut National de Santé Publique du Québec; Université Laval; Centre hospitalier universitaire de Québec; Institut National de la Recherche Scientifique","funders":"","keywords":"Hilbert–Huang transform; Scale (ratio); Regression analysis; Regression; Linear regression; Lag; Time series; Reliability (semiconductor); Econometrics; Computer science; Series (stratigraphy); Statistics; Data mining; Mathematics; Geography","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":[0.007927318,0.001458256,0.001870562,0.001255461,0.0006081864,0.001087457,0.002803318,0.001559921,0.004426089],"category_scores_gemma":[0.02010859,0.001081023,0.00259959,0.001800333,0.0006121531,0.0008543468,0.002265845,0.003084048,0.001259324],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000603366,"about_ca_system_score_gemma":0.00139405,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01894225,"about_ca_topic_score_gemma":0.0164422,"domain_scores_codex":[0.998021,0.001306223,0.0001599747,0.0002729789,0.0001562346,0.00008357182],"domain_scores_gemma":[0.9910382,0.007552719,0.0003325501,0.0005002444,0.0004423592,0.0001340112],"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":[0.0002219574,0.0001726659,0.00912165,0.0002019899,0.0005848033,0.0002005888,0.0001315839,0.8813277,0.001194921,0.007941122,0.003819451,0.09508155],"study_design_scores_gemma":[0.00001681661,0.00001892973,0.0007227688,0.000007787146,0.00002068011,0.0000304432,0.00001119673,0.9960784,0.0001748282,0.002069287,0.0008372524,0.00001154963],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02779675,0.0008215209,0.964685,0.0003434743,0.0001620253,0.00008783437,0.001326117,0.004286919,0.0004903168],"genre_scores_gemma":[0.2382081,0.0009154567,0.7520357,0.0002129663,0.0001780502,0.0006564549,0.00216899,0.001441176,0.004183175],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01894225,"threshold_uncertainty_score":0.04192412,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08597697209963745,"score_gpt":0.3106987766331796,"score_spread":0.2247218045335421,"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."}}