{"id":"W2336508894","doi":"10.1002/atr.1376","title":"Using stop bar detector information to determine turning movement proportions in shared lanes","year":2016,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Traffic control and management","field":"Engineering","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Intersection (aeronautics); Detector; Queue; Computer science; Traffic flow (computer networking); SIGNAL (programming language); Traffic volume; Bar (unit); Movement (music); Linear regression; Simulation; Statistics; Transport engineering; Real-time computing; Mathematics; Engineering; Geography; Telecommunications; Acoustics; Physics; Computer network","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":[0.00008228676,0.00007789749,0.0001214882,0.0002180965,0.00001661256,0.0000115074,0.00005102629,0.00002075279,0.00007290922],"category_scores_gemma":[0.00001755282,0.0000588835,0.00003973433,0.0001023146,0.000003568147,0.0008378553,0.000001086543,0.00005267068,0.00000395035],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001073064,"about_ca_system_score_gemma":0.00001328408,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003151074,"about_ca_topic_score_gemma":0.000115319,"domain_scores_codex":[0.9992142,0.000004994836,0.0004715221,0.00004339556,0.000150873,0.0001150208],"domain_scores_gemma":[0.9996949,0.0000181907,0.000119233,0.00004986263,0.00006646658,0.00005129238],"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":[0.00005021016,0.00001040346,0.0002984418,0.00003800534,0.00001488334,0.00001025342,0.0009069387,0.8228847,0.06281036,0.00002090341,0.00003359474,0.1129214],"study_design_scores_gemma":[0.004837197,0.0003277501,0.9527692,0.001020754,0.00008415319,0.000007470831,0.001042421,0.006320647,0.007199436,0.0002322494,0.02573673,0.0004220297],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9197305,0.00002894421,0.07962031,0.0001204973,0.0002252736,0.0001875021,0.00002925076,0.00003470454,0.00002301454],"genre_scores_gemma":[0.9894968,0.00002373044,0.01036157,0.0000414306,0.00003867027,0.00001091444,0.000009971872,0.000007983883,0.000008904984],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9524707,"threshold_uncertainty_score":0.2401199,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009433261679425008,"score_gpt":0.2210170298733151,"score_spread":0.2115837681938901,"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."}}