{"id":"W2809529282","doi":"10.1109/taes.2018.2848360","title":"Unambiguous Sparse Recovery of Migrating Targets With a Robustified Bayesian Model","year":2018,"lang":"en","type":"article","venue":"IEEE Transactions on Aerospace and Electronic Systems","topic":"Target Tracking and Data Fusion in Sensor Networks","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Division of Grants and Agreements; Ontario Ministry of Research, Innovation and Science","keywords":"Clutter; Gibbs sampling; Bayesian probability; Autoregressive model; Computer science; Algorithm; Grid; Radar; Noise (video); Artificial intelligence; Bayesian inference; Pattern recognition (psychology); Mathematics; Statistics; Telecommunications","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002704914,0.0002275711,0.0003045646,0.0001280569,0.0003184578,0.0001296163,0.0003071808,0.0001232088,0.000005277455],"category_scores_gemma":[0.000003041634,0.0001898714,0.00006106324,0.0004042111,0.0001058758,0.0002492029,0.000003303709,0.0002991365,0.000008204913],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006492074,"about_ca_system_score_gemma":0.0001439875,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003598735,"about_ca_topic_score_gemma":0.0005853042,"domain_scores_codex":[0.9983044,0.00007719161,0.0002987964,0.0005013996,0.0002851846,0.0005330222],"domain_scores_gemma":[0.9989347,0.00008031609,0.0001570273,0.0005902702,0.0001215777,0.0001160545],"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":[0.0003336782,0.0002880642,0.00004403999,0.00009899584,0.000200033,0.00001090769,0.001726615,0.9640264,0.009657149,0.006653578,0.001536707,0.01542386],"study_design_scores_gemma":[0.0006157067,0.001104942,0.00001199362,0.0002106403,0.0000352773,0.0000984088,0.0001230207,0.9859792,0.01104775,0.0001633141,0.0002989756,0.0003107598],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03781114,0.0003977375,0.9605539,0.0001577042,0.0004421123,0.0002283208,0.00001250962,0.0001394325,0.0002571636],"genre_scores_gemma":[0.9910883,0.0001823743,0.007849121,0.00006225007,0.00007039776,0.00002874785,0.000001581859,0.00002280438,0.0006943909],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9532772,"threshold_uncertainty_score":0.774273,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01131502582821696,"score_gpt":0.2122410670043046,"score_spread":0.2009260411760877,"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."}}