{"id":"W2057931901","doi":"10.1214/13-aoas684","title":"Beta regression for time series analysis of bounded data, with application to Canada Google® Flu Trends","year":2014,"lang":"en","type":"article","venue":"The Annals of Applied Statistics","topic":"Advanced Statistical Process Monitoring","field":"Decision Sciences","cited_by":64,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Bounded function; Autoregressive model; Mathematics; Econometrics; Statistics; Autoregressive integrated moving average; Regression analysis; Time series; Computer science","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001452133,0.0001724518,0.000616824,0.0002206737,0.0001804656,0.00004802284,0.001273742,0.00003164459,0.00003208237],"category_scores_gemma":[0.001097169,0.000103817,0.00003160731,0.001572036,0.0001974144,0.00008947631,0.0002006757,0.00007725266,0.000004691998],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001958248,"about_ca_system_score_gemma":0.0001523039,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003887814,"about_ca_topic_score_gemma":0.04053688,"domain_scores_codex":[0.9972239,0.00005312095,0.0007394939,0.0004823553,0.00123718,0.0002639812],"domain_scores_gemma":[0.993551,0.0034653,0.0006522527,0.001444438,0.0007579629,0.0001290643],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.001689504,0.00008747917,0.0002206128,0.00006814502,0.0005326014,0.000001005041,0.0006066966,0.0301188,0.001800302,0.2213292,0.03027077,0.7132748],"study_design_scores_gemma":[0.001018011,0.0008197451,0.01472136,0.00006472851,0.001655466,0.000001647598,0.001282276,0.3332158,0.0251427,0.4515965,0.1695379,0.0009437572],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.003132759,0.00001683618,0.9891651,0.0007094865,0.00003364999,0.0002461215,0.005546974,0.0000119691,0.001137118],"genre_scores_gemma":[0.7515092,0.000006294769,0.2468514,0.0002161142,0.0000593539,0.00005203658,0.0005470035,0.00002348016,0.000735034],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7483765,"threshold_uncertainty_score":0.9769709,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1102783874309793,"score_gpt":0.4260513457302483,"score_spread":0.315772958299269,"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."}}