{"id":"W2042426056","doi":"10.1117/12.445381","title":"Detection, recognition, identification, and tracking of military vehicles using biomimetic intelligence","year":2001,"lang":"en","type":"article","venue":"Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE","topic":"Advanced Measurement and Detection Methods","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Research & Development Corporation; Defence Research and Development Canada","funders":"","keywords":"Automatic target recognition; Computer science; Clutter; Artificial intelligence; Identification (biology); Computer vision; Tracking (education); Object detection; Pattern recognition (psychology); Target acquisition; Synthetic aperture radar; Radar; Telecommunications","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":[0.0007501745,0.0002308703,0.0003073516,0.0001725322,0.00008377479,0.00003100356,0.0003221997,0.0001391166,0.000008141832],"category_scores_gemma":[0.0005353678,0.0002217663,0.0002696193,0.0004860857,0.0001985133,0.0005911745,0.00004663644,0.0002018743,5.613261e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001058722,"about_ca_system_score_gemma":0.00001108017,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000008485662,"about_ca_topic_score_gemma":3.663505e-7,"domain_scores_codex":[0.9983031,3.276516e-8,0.0007826929,0.000265069,0.0004058111,0.0002433497],"domain_scores_gemma":[0.9979675,0.0001217996,0.0002194294,0.00005050207,0.001560557,0.00008023825],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00005119219,0.00003983039,0.0003575623,0.0005109078,0.0002098132,3.876058e-8,0.0001624993,0.001830101,0.9802358,0.003965326,0.00005160745,0.01258533],"study_design_scores_gemma":[0.0003224965,0.00009510056,0.001073745,0.0002369674,0.0001322845,0.00002528629,0.0009173021,0.1028536,0.8900492,0.003639521,0.0004081283,0.0002463622],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9891058,0.0006528784,0.009196675,0.000130945,0.0002736291,0.0003335839,0.00001707605,0.0001081227,0.0001812749],"genre_scores_gemma":[0.8593113,0.0007277853,0.1396184,0.00001583557,0.0001959433,0.00004905791,0.000002944681,0.00005198494,0.00002676582],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1304217,"threshold_uncertainty_score":0.9043369,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0334038663054368,"score_gpt":0.2645699643873577,"score_spread":0.2311660980819209,"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."}}