{"id":"W4403059761","doi":"10.1109/ieeedata.2024.3471469","title":"Descriptor: Simon Fraser University Electric Vehicle Parking Dataset (SFU-EVP)","year":2024,"lang":"en","type":"article","venue":"IEEE data descriptions.","topic":"Vehicle License Plate Recognition","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University","funders":"Natural Resources Canada; Natural Sciences and Engineering Research Council of Canada","keywords":"Aeronautics; Electric vehicle; Transport engineering; Computer science; Automotive engineering; Engineering; Physics","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"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":[0.000759921,0.002464687,0.001590242,0.003566484,0.001033198,0.002276817,0.002952699,0.002485309,0.0420526],"category_scores_gemma":[0.003842624,0.0004719903,0.001363761,0.006577886,0.0003899831,0.001988404,0.001814583,0.001807814,0.07185902],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001905928,"about_ca_system_score_gemma":0.00230248,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.05180724,"about_ca_topic_score_gemma":0.06332723,"domain_scores_codex":[0.9988637,0.0001342532,0.0001247738,0.0002962994,0.0003530579,0.0002279117],"domain_scores_gemma":[0.9984326,0.000245628,0.0001206764,0.000377375,0.0006232055,0.0002005721],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0000383133,0.00001737042,0.0007982349,0.0001985947,0.00001709427,0.00002239218,0.00001032049,0.0004732761,0.00007430847,0.000279013,0.9957639,0.002307085],"study_design_scores_gemma":[0.0002217524,0.0000332519,0.008032968,0.0002284362,0.00002602411,0.00008861593,0.0001887168,0.003953186,0.0007098228,0.001553953,0.9849067,0.00005659436],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0003027238,0.00004269295,0.00007277221,0.00009576324,0.00004513207,0.00001200978,0.9980935,0.0005416016,0.0007936058],"genre_scores_gemma":[0.0005430182,0.00003040347,0.0001983276,0.00002516482,0.00000787028,0.0000283689,0.9988064,0.00003293236,0.0003274516],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.05180724,"threshold_uncertainty_score":0.14068,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04244847937209739,"score_gpt":0.2351234023102891,"score_spread":0.1926749229381917,"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."}}