{"id":"W2039190250","doi":"10.1002/atr.128","title":"Analysis of peak and non‐peak traffic forecasts using combined models","year":2010,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Traffic Prediction and Management Techniques","field":"Engineering","cited_by":34,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Computer science; Selection (genetic algorithm); Field (mathematics); Index (typography); Sample (material); Baseline (sea); Model selection; Traffic count; Operations research; Econometrics; Data mining; Machine learning; Engineering; Transport engineering; Mathematics; Traffic volume","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001956759,0.0009159842,0.0009562848,0.001347287,0.0003177583,0.001239243,0.0007057344,0.0006485531,0.001274438],"category_scores_gemma":[0.006423298,0.0004708381,0.001142769,0.001015279,0.0002619028,0.001258307,0.001017933,0.001050623,0.0002838944],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005668505,"about_ca_system_score_gemma":0.0007621886,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01135124,"about_ca_topic_score_gemma":0.008988447,"domain_scores_codex":[0.9989762,0.000358646,0.00004314585,0.0001528586,0.0003668352,0.0001023665],"domain_scores_gemma":[0.9961188,0.00263784,0.0002751452,0.0002210757,0.0006107811,0.0001363872],"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":[0.0004113571,0.00007566646,0.01389799,0.00006582504,0.0003023606,0.0001099119,0.0000717428,0.938534,0.002168261,0.0009428781,0.0006147801,0.04280533],"study_design_scores_gemma":[0.000004776953,0.00003033593,0.001773294,0.000002817048,0.00002589901,0.000007936249,0.00001205635,0.9974069,0.0003491254,0.0002976193,0.00008239359,0.000006790791],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7519677,0.0006030569,0.241469,0.0003647248,0.0001296005,0.00008476073,0.0006737551,0.0005806327,0.004126802],"genre_scores_gemma":[0.9820119,0.0001108611,0.01647248,0.00001297824,0.00004216923,0.00004030429,0.0003988233,0.00002509704,0.0008853489],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01135124,"threshold_uncertainty_score":0.02257031,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008171149146348882,"score_gpt":0.2258524791928426,"score_spread":0.2176813300464937,"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."}}