{"id":"W2026552738","doi":"10.1016/j.sbspro.2012.04.134","title":"Improving Group Assignment and AADT Estimation Accuracy of Short-term Traffic Counts using Historical Seasonal Patterns & Bayesian Statistics","year":2012,"lang":"en","type":"article","venue":"Procedia - Social and Behavioral Sciences","topic":"Traffic Prediction and Management Techniques","field":"Engineering","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo; University of New Brunswick","funders":"Natural Sciences and Engineering Research Council of Canada; Federal Highway Administration","keywords":"Traffic count; Statistics; Term (time); Bayesian probability; Recreation; Matching (statistics); Estimation; Computer science; Bayes' theorem; Transport engineering; Mathematics; Traffic congestion; Engineering; Ecology","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":[0.0001123615,0.00006275973,0.00007125075,0.00003410529,0.0001070997,0.00003173186,0.0000394204,0.00003490808,0.000009920955],"category_scores_gemma":[0.000004823326,0.00005741436,0.00001042214,0.00005859273,0.00006211762,0.0002224018,0.00001909974,0.00004462402,2.830617e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006912126,"about_ca_system_score_gemma":0.000008639217,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002422347,"about_ca_topic_score_gemma":0.00001256535,"domain_scores_codex":[0.9995329,0.000005898037,0.00009908736,0.00007712759,0.0001575623,0.0001273596],"domain_scores_gemma":[0.9998888,0.000009415128,0.0000271123,0.00001695234,0.000009726632,0.0000479743],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.000003447076,0.0001403536,0.1008975,0.0002515398,0.000007044008,7.252595e-7,0.001724949,0.00005816384,0.003273118,0.000672911,0.001588567,0.8913817],"study_design_scores_gemma":[0.000613025,0.0003701686,0.5018736,0.0001587088,0.0004612074,0.00002420308,0.0009353029,0.4898931,0.0005795992,0.0002318022,0.003631718,0.001227588],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8398328,0.0001205303,0.1594717,0.00001038461,0.0002164786,0.00009464792,0.00002070561,0.0001893736,0.00004338181],"genre_scores_gemma":[0.9923649,0.00003044666,0.007510046,0.000003762975,0.00006836907,0.000007063715,0.000006364703,0.00000425563,0.000004836448],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8901541,"threshold_uncertainty_score":0.234129,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06170468832307961,"score_gpt":0.3209936521643644,"score_spread":0.2592889638412847,"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."}}