{"id":"W2593332226","doi":"10.1002/bimj.201600137","title":"Mixture Markov regression model with application to mosquito surveillance data analysis","year":2017,"lang":"en","type":"article","venue":"Biometrical Journal","topic":"Bayesian Methods and Mixture Models","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Public Health; Public Health Agency of Canada; York University","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research","keywords":"Series (stratigraphy); Markov chain; Mixture model; Regression analysis; Markov model; Statistics; Time series; Expectation–maximization algorithm; Computer science; Mathematics; Maximum likelihood; Econometrics","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":[0.00561221,0.000804795,0.001792454,0.001770259,0.00066265,0.001325467,0.002264765,0.001473145,0.002194016],"category_scores_gemma":[0.01227013,0.000878342,0.002173819,0.002671073,0.0007772672,0.001960278,0.001387096,0.002241309,0.0006932159],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001117362,"about_ca_system_score_gemma":0.001467711,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01963807,"about_ca_topic_score_gemma":0.0108727,"domain_scores_codex":[0.9977238,0.001191111,0.0001133274,0.0004345326,0.0003868225,0.0001503987],"domain_scores_gemma":[0.9943594,0.004304477,0.0004291179,0.0002353652,0.0005765411,0.00009511262],"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":[0.0001427671,0.00007423873,0.00484143,0.0001739484,0.0003019438,0.0002580328,0.0002645555,0.8328526,0.001692554,0.079447,0.002099767,0.07785118],"study_design_scores_gemma":[0.000004691864,0.00001179131,0.0003874295,0.000006984846,0.00001693533,0.00003100836,0.000007682042,0.9904174,0.0001092189,0.008498977,0.0004944809,0.00001327271],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.008818833,0.000468341,0.9897266,0.0001899255,0.00002863777,0.00003065906,0.0001034233,0.0002426293,0.000390797],"genre_scores_gemma":[0.4570205,0.002577124,0.5309103,0.0002140251,0.0002343039,0.0004856905,0.001401554,0.0003315635,0.006824939],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01963807,"threshold_uncertainty_score":0.03904754,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04277786468013042,"score_gpt":0.3494344604817212,"score_spread":0.3066565958015908,"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."}}