{"id":"W4411925388","doi":"10.2514/1.i011556","title":"Air Target Intention Recognition via Bidirectional Long Short-Term Memory Networks and Hierarchical Maneuver Feature Extraction","year":2025,"lang":"en","type":"article","venue":"Journal of Aerospace Information Systems","topic":"Aerospace and Aviation Technology","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"Fundamental Research Funds for the Central Universities; National Natural Science Foundation of China","keywords":"Term (time); Computer science; Feature extraction; Long short term memory; Artificial intelligence; Feature (linguistics); Pattern recognition (psychology); Extraction (chemistry); Speech recognition; Artificial neural network; Recurrent neural network; Physics","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":[0.0002337669,0.0007541121,0.0003356381,0.0004726019,0.0001979234,0.0004202322,0.0005708644,0.0003782315,0.000906083],"category_scores_gemma":[0.0006765846,0.0002188296,0.0005287215,0.0004241762,0.0002067857,0.0008731068,0.0005712653,0.0007115107,0.0004253853],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002626797,"about_ca_system_score_gemma":0.0004054899,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005122004,"about_ca_topic_score_gemma":0.006823137,"domain_scores_codex":[0.9998388,0.0000172618,0.00001185667,0.00005295149,0.00004709734,0.00003199567],"domain_scores_gemma":[0.9998468,0.00003804556,0.0000282849,0.00002037374,0.00005637858,0.00001012827],"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":[0.0003041379,0.0002413961,0.003875473,0.0001536331,0.0001191886,0.0002648595,0.0001867745,0.1127455,0.06839439,0.003095681,0.00328974,0.8073292],"study_design_scores_gemma":[0.000007486621,0.00009345813,0.0018851,0.000009439855,0.000036136,0.00006086728,0.0000311431,0.9822542,0.01246558,0.0024876,0.0006541759,0.00001485838],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1155944,0.0006609615,0.8780046,0.0001733434,0.0001221877,0.00006389171,0.0002631336,0.001800266,0.00331711],"genre_scores_gemma":[0.9063339,0.0004008122,0.08849267,0.0001464227,0.00004695643,0.0001152888,0.0006959873,0.00006398297,0.003703912],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005122004,"threshold_uncertainty_score":0.01018441,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005883748297925735,"score_gpt":0.2186619464252041,"score_spread":0.2127781981272784,"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."}}