{"id":"W4383961766","doi":"10.1061/jtepbs.teeng-7532","title":"Method for Detection and Classification of Turning Movements in Intersections Using Bluetooth Low-Energy Signals","year":2023,"lang":"en","type":"article","venue":"Journal of Transportation Engineering Part A Systems","topic":"Vehicle emissions and performance","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"","keywords":"Intersection (aeronautics); SIGNAL (programming language); Bluetooth; Energy (signal processing); Computer science; Computer vision; Bluetooth Low Energy; Artificial intelligence; Transmission (telecommunications); Line (geometry); Real-time computing; Simulation; Wireless; Engineering; Telecommunications; Mathematics; Statistics","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004576225,0.0006556467,0.0006032519,0.00264163,0.0002853383,0.0005815601,0.0009075138,0.0006215421,0.001227397],"category_scores_gemma":[0.001194694,0.0002402845,0.0004018976,0.0009926311,0.0001968234,0.0004521682,0.0004561424,0.0005238583,0.001262702],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001646239,"about_ca_system_score_gemma":0.00035109,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007622085,"about_ca_topic_score_gemma":0.001201969,"domain_scores_codex":[0.9991336,0.0001051097,0.0000542022,0.0002006169,0.0004367804,0.00006970088],"domain_scores_gemma":[0.9992422,0.0001495061,0.0001166556,0.00007708491,0.000384414,0.00003024602],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0004404728,0.0002012152,0.02056659,0.0005620923,0.0001188259,0.0004388508,0.0002965932,0.005285447,0.1615653,0.001021113,0.003263762,0.8062397],"study_design_scores_gemma":[0.0002412588,0.00158455,0.1277146,0.0002351835,0.0003582544,0.006308574,0.0009931506,0.5035226,0.3238067,0.002619342,0.03223881,0.0003769841],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06335486,0.0005153691,0.9293173,0.00007627139,0.0001642763,0.000378061,0.0003603347,0.00310618,0.002727392],"genre_scores_gemma":[0.4312997,0.0006828274,0.5600021,0.0001320496,0.00009070271,0.0007786214,0.0008750974,0.0001057678,0.006033144],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00264163,"threshold_uncertainty_score":0.004106104,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02564596261773466,"score_gpt":0.2657326914763967,"score_spread":0.2400867288586621,"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."}}