{"id":"W4403277904","doi":"10.1109/taslp.2024.3477277","title":"Smoothed Frame-Level SINR and Its Estimation for Sensor Selection in Distributed Acoustic Sensor Networks","year":2024,"lang":"en","type":"article","venue":"IEEE/ACM Transactions on Audio Speech and Language Processing","topic":"Energy Efficient Wireless Sensor Networks","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Institut National de la Recherche Scientifique; Université du Québec à Montréal","funders":"","keywords":"Selection (genetic algorithm); Estimation; Frame (networking); Computer science; Acoustic sensor; Wireless sensor network; Acoustics; Artificial intelligence; Engineering; Telecommunications; Computer 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":[0.001258945,0.0009540247,0.0008218505,0.0005683096,0.0003172948,0.0005668044,0.0009557947,0.0005800511,0.0008153932],"category_scores_gemma":[0.005565329,0.0003036588,0.0004737945,0.0006450763,0.000437442,0.0009100842,0.0007555528,0.0008309979,0.0004918193],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006016255,"about_ca_system_score_gemma":0.0008784743,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002739196,"about_ca_topic_score_gemma":0.003329976,"domain_scores_codex":[0.9991252,0.0002910828,0.00004924894,0.0001721275,0.0002871308,0.00007525525],"domain_scores_gemma":[0.9986086,0.0006656228,0.0001521467,0.000102857,0.000418087,0.00005272434],"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":[0.0002293259,0.0000640258,0.001959402,0.0001591211,0.00006455197,0.0001414548,0.0001493419,0.7642204,0.01909207,0.009386544,0.002304643,0.2022291],"study_design_scores_gemma":[0.000003120982,0.00003291923,0.0003490676,0.00000610085,0.000008289176,0.00003237384,0.00001300077,0.9952902,0.002200177,0.001608894,0.0004479448,0.000007849599],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.006456542,0.0002098306,0.9926059,0.00005475252,0.00004323325,0.00001335029,0.00003108568,0.0001856706,0.0003995553],"genre_scores_gemma":[0.5875983,0.0008119929,0.4082003,0.0001651558,0.0002278346,0.0001303482,0.0003849528,0.0001296092,0.002351565],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002739196,"threshold_uncertainty_score":0.006658018,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01629842000171631,"score_gpt":0.2674018257904193,"score_spread":0.2511034057887029,"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."}}