{"id":"W2999068022","doi":"10.5815/ijisa.2019.11.03","title":"Parallel Implementation of a Video-based Vehicle Speed Measurement System for Municipal Roadways","year":2019,"lang":"en","type":"article","venue":"International Journal of Intelligent Systems and Applications","topic":"Vehicle License Plate Recognition","field":"Engineering","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"","keywords":"Computer science; CUDA; Intelligent transportation system; License; Process (computing); Speedup; Real-time computing; Parallel processing; Massively parallel; Parallel computing","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":[0.0002480366,0.0005022216,0.0004815035,0.0006506416,0.000385568,0.00061779,0.001100637,0.0003082261,0.004503957],"category_scores_gemma":[0.0006069512,0.0002275414,0.0002964615,0.0005379643,0.0001895794,0.0004115271,0.0003430277,0.0003688645,0.001102054],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008722433,"about_ca_system_score_gemma":0.001276296,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01048502,"about_ca_topic_score_gemma":0.008521027,"domain_scores_codex":[0.9996891,0.00003691491,0.0000146092,0.00009553938,0.000115047,0.00004876129],"domain_scores_gemma":[0.9997055,0.00002747051,0.00002207745,0.00003904432,0.000171996,0.00003399915],"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":[0.002059042,0.0006615565,0.01480758,0.0004291488,0.0002557888,0.001083318,0.0006341728,0.1605792,0.2340848,0.00638754,0.02111934,0.5578984],"study_design_scores_gemma":[0.00009891415,0.0002715066,0.004113805,0.00001413911,0.00005015187,0.0001402589,0.00008296949,0.9256535,0.05979659,0.0006517714,0.009083475,0.0000428593],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2452661,0.0002364118,0.7154454,0.0002416209,0.0003264593,0.0004709405,0.0007756399,0.02340331,0.01383407],"genre_scores_gemma":[0.7738651,0.00007670215,0.2209643,0.00005794919,0.00003083187,0.0002031569,0.0006625821,0.0001798626,0.003959608],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01048502,"threshold_uncertainty_score":0.02084798,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03109389868361305,"score_gpt":0.2815481925827696,"score_spread":0.2504542938991565,"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."}}