{"id":"W2133728325","doi":"10.1109/taes.2011.5705691","title":"Separable Approximation for Solving the Sensor Subset Selection Problem","year":2011,"lang":"en","type":"article","venue":"IEEE Transactions on Aerospace and Electronic Systems","topic":"Distributed Sensor Networks and Detection Algorithms","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Mathematics; Cluster analysis; Mathematical optimization; Separable space; Approximation algorithm; Selection (genetic algorithm); Metric (unit); Fisher information; Graph; Algorithm; Combinatorics; Computer science; Artificial intelligence; Statistics","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":[0.0004317707,0.000188472,0.0001800015,0.00008198791,0.0006914843,0.0002524384,0.0002091526,0.000112282,0.000005778491],"category_scores_gemma":[0.000002388894,0.0001452632,0.0000899938,0.0004632577,0.00003324843,0.0002834573,0.000001596191,0.0002634891,0.00001401595],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001236995,"about_ca_system_score_gemma":0.00006525298,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001880544,"about_ca_topic_score_gemma":0.0001269748,"domain_scores_codex":[0.9985862,0.00006925416,0.0002413591,0.0003953945,0.0001851637,0.0005225659],"domain_scores_gemma":[0.9993129,0.000092164,0.0001221685,0.0002787214,0.0001177279,0.00007626049],"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":[0.001428827,0.002464941,0.0001917157,0.001065142,0.00217466,0.00001107723,0.01734079,0.4396244,0.04231143,0.2226717,0.02193531,0.2487801],"study_design_scores_gemma":[0.0007584076,0.0007258296,0.00002632796,0.00004701515,0.00005312071,0.0001657329,0.0003654026,0.9709626,0.01842066,0.0008370615,0.007289366,0.0003484121],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.004049593,0.00022769,0.9935213,0.0002151259,0.0005868712,0.0008478599,0.000008158042,0.0002317669,0.0003116596],"genre_scores_gemma":[0.9946055,0.000137695,0.003354311,0.000079613,0.00008006544,0.0003645031,0.000001999194,0.00001966043,0.001356691],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9905559,"threshold_uncertainty_score":0.5923663,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01884670896801379,"score_gpt":0.2164321446054638,"score_spread":0.19758543563745,"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."}}