{"id":"W4390045581","doi":"10.1109/idaacs58523.2023.10348936","title":"An Aircraft Identification System Using Convolution Neural Networks","year":2023,"lang":"en","type":"article","venue":"","topic":"Vehicle License Plate Recognition","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"","keywords":"Computer science; Convolutional neural network; Identification (biology); Artificial intelligence; Convolution (computer science); Artificial neural network; Task (project management); Pattern recognition (psychology); Computer vision; Radar; Feature extraction; Deep learning; 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":[0.0001630927,0.00008797993,0.00008391171,0.0001145628,0.00007020363,0.00006187912,0.00006145484,0.00008495423,0.00001373059],"category_scores_gemma":[0.000003362925,0.00009855759,0.00002685737,0.0003783045,0.000008974315,0.0003623701,0.000007939464,0.00008381313,0.0002161371],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000109608,"about_ca_system_score_gemma":0.000003002244,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002922673,"about_ca_topic_score_gemma":0.00001100049,"domain_scores_codex":[0.9993346,0.00003028216,0.00019583,0.0001340146,0.00009924489,0.0002060412],"domain_scores_gemma":[0.999705,0.00001916679,0.00002263577,0.000156272,0.00004030728,0.00005658878],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00000188248,0.000003354062,0.0003070578,0.00005540785,0.00001051973,0.000004753199,0.00003838827,0.9571223,0.03737743,0.0001827469,0.0001367452,0.004759385],"study_design_scores_gemma":[0.00009299223,0.000005610751,0.005772585,0.00001787788,0.00001460327,0.00002139338,0.0002020787,0.992089,0.001641778,0.00001181941,0.0000161874,0.0001141285],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.829386,0.00002170515,0.1667147,0.000006949463,0.0006869292,0.0001249356,0.000003458614,0.002738277,0.0003169727],"genre_scores_gemma":[0.9994306,0.000008024156,0.0001788998,0.000006577047,0.00018688,0.00001019695,0.0001211932,0.00003249466,0.00002515193],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1700446,"threshold_uncertainty_score":0.4019062,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02051220604491182,"score_gpt":0.2367426650218253,"score_spread":0.2162304589769135,"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."}}