{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002634592,0.00006517608,0.00008414151,0.00005962655,0.00005373703,0.0001212423,0.00005958221,0.00004763964,0.00002258775],"category_scores_gemma":[0.00002931056,0.00006817416,0.00001802096,0.0001914342,0.00001329185,0.00008445245,0.0000239688,0.00007539904,0.0001959695],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001212774,"about_ca_system_score_gemma":0.00001284251,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000008488416,"about_ca_topic_score_gemma":0.00001467212,"domain_scores_codex":[0.9992697,0.00004588193,0.0002148092,0.0001625818,0.0001680058,0.0001390166],"domain_scores_gemma":[0.9995658,0.000051035,0.00001664188,0.0002365081,0.00007752601,0.00005253186],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000004090505,0.00002524763,0.02893889,0.001192698,0.00009236142,0.00002113684,0.0007013437,0.00139065,0.8845611,0.0282729,0.003748036,0.05105149],"study_design_scores_gemma":[0.0001584181,0.000005396659,0.07999024,0.000152333,0.00001227799,0.00006886842,0.00322235,0.6481083,0.2434254,0.00003983679,0.02458723,0.0002293631],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8885976,0.001311488,0.06437762,0.000138163,0.0008343888,0.0002576339,0.000002681831,0.0008801992,0.04360023],"genre_scores_gemma":[0.9987457,0.00005137238,0.0001902022,0.000003946305,0.00007533778,0.00003237621,0.000008808809,0.0000179,0.0008743465],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6467176,"threshold_uncertainty_score":0.2780062,"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."}}