{"id":"W4413089326","doi":"10.1016/j.geits.2025.100340","title":"Automating the Estimation of Turning Movement Rates at Multilane Roundabouts Using Unmanned Aerial Vehicles and Deep Learning","year":2025,"lang":"en","type":"article","venue":"Green Energy and Intelligent Transportation","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"University of Sharjah; Carleton University","keywords":"Movement (music); Artificial intelligence; Computer science; Aeronautics; Simulation; Engineering; Physics","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001269804,0.0001121751,0.0001342662,0.0000765685,0.0002144636,0.00001208292,0.00004763419,0.00009978857,0.000008279453],"category_scores_gemma":[0.000007759702,0.0000970749,0.00002421175,0.0001100473,0.00007552095,0.00009705462,0.000007503743,0.00009285169,2.21541e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003423588,"about_ca_system_score_gemma":0.000006900824,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003350536,"about_ca_topic_score_gemma":0.0006350057,"domain_scores_codex":[0.9993845,0.00002442298,0.0002906434,0.0001201093,0.00006102163,0.0001192688],"domain_scores_gemma":[0.9997652,0.00006572349,0.00006623277,0.00006308368,0.0000232053,0.00001662668],"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":[0.00004328268,0.000009928409,0.008508407,0.0001282321,0.00009168328,0.000001573976,0.00190655,0.6566169,0.01401012,0.007507785,9.52075e-7,0.3111746],"study_design_scores_gemma":[0.0001817814,0.00002490375,0.04617858,0.00007965424,0.00004847156,0.000001075527,0.0002524021,0.873574,0.07872526,0.0007162335,0.0001265875,0.00009107266],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8813719,0.0010939,0.1171943,0.00004246577,0.00006336269,0.00006538947,0.000002148316,0.0001220342,0.00004459045],"genre_scores_gemma":[0.998479,0.0004245347,0.0009507335,0.00002791311,0.00001112525,0.000008039669,0.00004482599,0.000009676991,0.0000441433],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3110836,"threshold_uncertainty_score":0.39586,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006926921183074839,"score_gpt":0.2197466708989526,"score_spread":0.2128197497158778,"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."}}