{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003605387,0.0005921103,0.0003186545,0.0004969664,0.0002534169,0.0004939071,0.0005760036,0.0004514033,0.003002872],"category_scores_gemma":[0.000506241,0.0002400606,0.000360374,0.000286271,0.0001246519,0.0005691409,0.0004738834,0.0004956335,0.001927332],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000547876,"about_ca_system_score_gemma":0.0005265052,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007256983,"about_ca_topic_score_gemma":0.007261148,"domain_scores_codex":[0.9997745,0.00001642948,0.00001256073,0.00008673004,0.00007002557,0.00003977233],"domain_scores_gemma":[0.9998094,0.00002814084,0.00002078515,0.00002882682,0.0001019511,0.00001097164],"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":[0.0005813706,0.0001975369,0.004500469,0.0001891577,0.0001603102,0.0002583108,0.00007663038,0.07344466,0.1639814,0.0019899,0.007703566,0.7469167],"study_design_scores_gemma":[0.00001367909,0.0001370978,0.004029857,0.0000230042,0.00004910363,0.0001728286,0.00001752499,0.9301993,0.05910375,0.000752078,0.005473065,0.00002873154],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1338141,0.0008026806,0.8336759,0.0002157519,0.0002886875,0.0001589227,0.0007625025,0.02125612,0.009025513],"genre_scores_gemma":[0.7358868,0.000310677,0.2476129,0.0002572161,0.00005140757,0.0001167491,0.001563315,0.0001477908,0.01405308],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007256983,"threshold_uncertainty_score":0.01442951,"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."}}