{"id":"W1920803581","doi":"10.1002/atr.1277","title":"Incorporating work zone configuration factors into speed‐flow and capacity models","year":2014,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Traffic control and management","field":"Engineering","cited_by":24,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Work zone; Work (physics); Speed limit; Flow (mathematics); Traffic flow (computer networking); Limit (mathematics); Mechanics; Environmental science; Computer science; Transport engineering; Engineering; Mathematics; Physics; Mechanical engineering; Computer network; Mathematical analysis","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001284895,0.00009769436,0.0001655335,0.00007779634,0.00003602822,0.00001852874,0.00003933276,0.00003443718,0.000003482582],"category_scores_gemma":[0.000007643883,0.00008836644,0.00004097193,0.00008654678,0.00001493201,0.0004076983,6.014156e-7,0.0001171436,4.421068e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002815852,"about_ca_system_score_gemma":0.000004808567,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000005339444,"about_ca_topic_score_gemma":0.00009479182,"domain_scores_codex":[0.9993716,0.00001107651,0.0003431736,0.00006614474,0.0001256996,0.00008229897],"domain_scores_gemma":[0.9996439,0.00003222711,0.0001440747,0.00004951658,0.00007171595,0.00005861711],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.00002687219,0.000008489642,0.0002465728,0.00004277696,0.00002439294,0.000001583153,0.001522134,0.943527,0.005817595,0.000910339,0.000009055953,0.04786319],"study_design_scores_gemma":[0.006320449,0.0005767894,0.6306697,0.0005218297,0.0003448223,0.000007210895,0.002126941,0.3259602,0.004932714,0.02435695,0.003305719,0.0008766293],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6940956,0.00008911185,0.3053894,0.00003937509,0.0002182858,0.00006228469,0.000001156026,0.00002736006,0.00007745617],"genre_scores_gemma":[0.9841149,0.00005494631,0.01571936,0.00001187304,0.00007106843,0.000001032639,0.000009013632,0.00001164269,0.000006110855],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6304231,"threshold_uncertainty_score":0.3603479,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009178917013479282,"score_gpt":0.1918553182556148,"score_spread":0.1826764012421355,"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."}}