{"id":"W2789834316","doi":"10.1109/ccwc.2018.8301714","title":"Vehicle make and model recognition using random forest classification for intelligent transportation systems","year":2018,"lang":"en","type":"article","venue":"","topic":"Vehicle License Plate Recognition","field":"Engineering","cited_by":23,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Regina","funders":"","keywords":"Scale-invariant feature transform; Computer science; Intelligent transportation system; Histogram; Random forest; Histogram of oriented gradients; Feature extraction; Artificial intelligence; Cognitive neuroscience of visual object recognition; Intelligent decision support system; Machine learning; Data mining; Image (mathematics); Engineering; Transport engineering","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":[0.0008662851,0.0009216579,0.0008467565,0.001986475,0.0005291097,0.0004882193,0.0008873635,0.0006736687,0.00202641],"category_scores_gemma":[0.001732246,0.0002376996,0.001404003,0.001218248,0.000204951,0.0007596658,0.000316382,0.0006999846,0.001230614],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005831412,"about_ca_system_score_gemma":0.0006927559,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01904285,"about_ca_topic_score_gemma":0.0169102,"domain_scores_codex":[0.9994203,0.0001000101,0.00004764627,0.0001579402,0.000166932,0.0001072553],"domain_scores_gemma":[0.9993299,0.0002148224,0.00006393148,0.00009777921,0.0002674276,0.00002608678],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003480683,0.0004016946,0.009133607,0.0001271217,0.0001505067,0.0001566806,0.00004120742,0.1846149,0.01922556,0.001450061,0.00712574,0.7772249],"study_design_scores_gemma":[0.00001043049,0.00007521428,0.003348866,0.00001044061,0.00002536814,0.00007915927,0.00001992347,0.987115,0.006673216,0.001273415,0.001347588,0.00002145067],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.09992575,0.0009531352,0.8853256,0.0002102966,0.0001596837,0.0002454759,0.001350672,0.009692623,0.002136734],"genre_scores_gemma":[0.6104136,0.0004189758,0.3805965,0.00007290832,0.00008266875,0.0002211261,0.005188081,0.0001912802,0.002814868],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01904285,"threshold_uncertainty_score":0.03786403,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06718069474515277,"score_gpt":0.258316000935421,"score_spread":0.1911353061902682,"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."}}