{"id":"W1976369180","doi":"10.1002/env.814","title":"Seasonal confounding and residual correlation in analyses of health effects of air pollution","year":2006,"lang":"en","type":"article","venue":"Environmetrics","topic":"Air Quality and Health Impacts","field":"Environmental Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada; British Columbia Centre for Disease Control","keywords":"Residual; Smoothing; Statistics; Air pollution; Confidence interval; Environmental science; Pollutant; Estimation; Pollution; Econometrics; Confounding; Covariate; Linear model; Mathematics","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":[0.0673832,0.0005644194,0.0009682485,0.001473532,0.0006892607,0.001175857,0.001283176,0.0008843257,0.0009757711],"category_scores_gemma":[0.1628689,0.0005837512,0.001512115,0.002259708,0.002409191,0.001083405,0.001818689,0.001330777,0.0001199053],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008458755,"about_ca_system_score_gemma":0.001335044,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009299933,"about_ca_topic_score_gemma":0.007613013,"domain_scores_codex":[0.9408753,0.05005763,0.001514085,0.004145383,0.002757112,0.0006505028],"domain_scores_gemma":[0.7736236,0.1934611,0.01288878,0.01726994,0.002106243,0.0006503157],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001428273,0.0001783736,0.8508167,0.000522139,0.00629804,0.0005370617,0.001781555,0.03641439,0.00332641,0.02265768,0.001458686,0.07458074],"study_design_scores_gemma":[0.00009985105,0.0009127129,0.8267988,0.0001246679,0.001188223,0.0003654515,0.0004578758,0.1216008,0.004119713,0.04013028,0.004111768,0.00008978513],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.602504,0.002671003,0.3908655,0.0007565322,0.000151271,0.0002630964,0.0007891027,0.0003785334,0.001620931],"genre_scores_gemma":[0.9694881,0.0002065249,0.02905871,0.0001477751,0.00006857287,0.0001750701,0.0003140668,0.0000556227,0.0004856579],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0673832,"threshold_uncertainty_score":0.3563607,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03986333621968186,"score_gpt":0.3330471868645418,"score_spread":0.2931838506448599,"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."}}