{"id":"W4389692377","doi":"10.1109/lra.2023.3342669","title":"Multibeam Forward-Looking Sonar Video Object Tracking Using Truncated - Sparsity and Aberrances Repression Regularization","year":2023,"lang":"en","type":"article","venue":"IEEE Robotics and Automation Letters","topic":"Advanced SAR Imaging Techniques","field":"Engineering","cited_by":7,"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":"Regularization (linguistics); Notation; Clutter; Mathematics; Algorithm; Artificial intelligence; Computer science","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":[0.0004952264,0.0006756394,0.000563954,0.0004506538,0.0002500196,0.0005747623,0.0009030238,0.0008989332,0.001067874],"category_scores_gemma":[0.001196111,0.0004132247,0.0006902382,0.0004566133,0.0003270348,0.001010108,0.0008932043,0.0008128111,0.0007668564],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003863005,"about_ca_system_score_gemma":0.0007279745,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00238627,"about_ca_topic_score_gemma":0.002929456,"domain_scores_codex":[0.9996368,0.00005996958,0.00001870635,0.00008240851,0.0001758907,0.0000262975],"domain_scores_gemma":[0.9995399,0.0001354912,0.0000748024,0.00006963439,0.0001484108,0.00003173998],"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":[0.0003169827,0.00008641923,0.001694838,0.0002286695,0.0001279694,0.0001667039,0.0003016285,0.1995819,0.2656514,0.01397289,0.003791589,0.514079],"study_design_scores_gemma":[0.00001097254,0.00003596328,0.0003739552,0.000007140889,0.00001326488,0.00008748229,0.00001151354,0.9799544,0.01660702,0.001164167,0.001716412,0.00001771814],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.006122008,0.00008136276,0.9929778,0.00005877758,0.00001907151,0.00001161207,0.00001703785,0.0001980806,0.000514252],"genre_scores_gemma":[0.1223693,0.0003126908,0.8710514,0.0001415358,0.00005132257,0.00008955665,0.000255446,0.0001159681,0.005612712],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00238627,"threshold_uncertainty_score":0.004744768,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01896488930571804,"score_gpt":0.2546376391460305,"score_spread":0.2356727498403125,"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."}}