{"id":"W2280398094","doi":"10.1002/sim.6904","title":"A latent process model for forecasting multiple time series in environmental public health surveillance","year":2016,"lang":"en","type":"article","venue":"Statistics in Medicine","topic":"Air Quality and Health Impacts","field":"Environmental Science","cited_by":17,"is_retracted":false,"has_abstract":true,"ca_institutions":"BC Centre for Disease Control; University of British Columbia; McGill University; McGill University Health Centre","funders":"Canadian Institutes of Health Research; Health Canada; British Columbia Centre for Disease Control; University of British Columbia","keywords":"Univariate; Bivariate analysis; Environmental epidemiology; Laplace's method; Computer science; Bayesian probability; Outcome (game theory); Econometrics; Statistics; Multivariate statistics; Environmental health; Machine learning; Mathematics; Medicine; Artificial intelligence","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001668411,0.0001293071,0.0002805853,0.00008082548,0.00008962662,0.000004745342,0.0001283112,0.00004635288,0.0003572657],"category_scores_gemma":[0.001115521,0.00009112733,0.000009277951,0.0001619934,0.0003012354,0.0001641073,0.0000497402,0.00009630182,0.00003083039],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004155199,"about_ca_system_score_gemma":0.00005448758,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002308911,"about_ca_topic_score_gemma":0.002917981,"domain_scores_codex":[0.9982966,0.00008385404,0.0004817879,0.0002743474,0.0002957093,0.0005677625],"domain_scores_gemma":[0.9990337,0.000455459,0.000156023,0.0001350378,0.000005090412,0.0002146744],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002981439,0.0003549973,0.7920362,0.0004404177,0.00000866527,0.00001998453,0.0147269,0.00480431,0.0004431507,0.0009472541,0.02648766,0.1594324],"study_design_scores_gemma":[0.003520847,0.0006444225,0.1726542,0.0002277095,0.000001892322,0.000008625708,0.0003717994,0.8020877,0.00001295189,0.01806815,0.002134653,0.0002670717],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1831746,0.0001355276,0.7632034,0.04962095,0.0001633121,0.001768037,0.001462471,0.00005143517,0.0004202563],"genre_scores_gemma":[0.9656715,0.0002225411,0.03162719,0.001759479,0.00004193058,0.00007149716,0.00007102526,0.00001985059,0.0005149306],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7972834,"threshold_uncertainty_score":0.3911809,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09898888752182965,"score_gpt":0.3413695372905244,"score_spread":0.2423806497686948,"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."}}