{"id":"W3185598201","doi":"10.1109/metroautomotive50197.2021.9502886","title":"Intelligent Parking Vehicle Identification and Classification System","year":2021,"lang":"en","type":"article","venue":"","topic":"Smart Parking Systems Research","field":"Engineering","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa","funders":"","keywords":"Computer science; Feature extraction; Identification (biology); MATLAB; Parking lot; Parking space; Intelligent transportation system; Matching (statistics); Artificial intelligence; Contextual image classification; Real-time computing; Pattern recognition (psychology); Engineering; Image (mathematics); 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":[0.0003806104,0.0004192091,0.0008069586,0.001243312,0.0006687501,0.0008569211,0.0009667446,0.0008184193,0.003124094],"category_scores_gemma":[0.0006503985,0.0002725628,0.0003343022,0.00062175,0.0002532088,0.0009901172,0.0005079047,0.0004003366,0.002225889],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003764525,"about_ca_system_score_gemma":0.0007062912,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002382676,"about_ca_topic_score_gemma":0.001886157,"domain_scores_codex":[0.9995066,0.00005028178,0.00003452614,0.0001632175,0.0001642642,0.00008106502],"domain_scores_gemma":[0.9996156,0.0000506307,0.00004391641,0.0000557417,0.0002055013,0.00002857011],"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":[0.0006356264,0.0003709366,0.01017021,0.0002828201,0.00008756395,0.0004179961,0.0002670716,0.04716785,0.08549152,0.005682376,0.0133085,0.8361176],"study_design_scores_gemma":[0.00008317681,0.0003538141,0.009084614,0.0000418594,0.0001214679,0.000871836,0.0001320537,0.8985381,0.0642826,0.003140107,0.0232407,0.0001097496],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08994059,0.000532956,0.886418,0.0002585286,0.0003595797,0.0001901154,0.0002578579,0.01260201,0.009440401],"genre_scores_gemma":[0.8317377,0.0002577339,0.1543938,0.0003246805,0.0001211814,0.0002042184,0.0005714538,0.0001048447,0.01228443],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003124094,"threshold_uncertainty_score":0.01045114,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03940012091889804,"score_gpt":0.2720889916923387,"score_spread":0.2326888707734407,"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."}}