{"id":"W2006987799","doi":"10.1155/2014/524740","title":"Application of Compressive Sampling in Computer Based Monitoring of Power Systems","year":2014,"lang":"en","type":"article","venue":"Advances in Computer Engineering","topic":"Sparse and Compressive Sensing Techniques","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ontario Tech University","funders":"","keywords":"Compressed sensing; Nyquist–Shannon sampling theorem; Nyquist rate; Sampling (signal processing); SIGNAL (programming language); Computer science; Power (physics); Data acquisition; Sampling theory; Electronic engineering; Algorithm; Computer vision; Mathematics; Statistics; Engineering; Physics; Sample size determination","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":[0.0001282867,0.000176722,0.000371536,0.0002935187,0.000008105494,0.00001135514,0.0002082357,0.0000702752,3.732978e-7],"category_scores_gemma":[0.000006542209,0.000201024,0.00004251301,0.0002584029,0.00001839758,0.0001700141,0.00004619256,0.0001625526,5.06783e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005038946,"about_ca_system_score_gemma":0.000003884542,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001807175,"about_ca_topic_score_gemma":0.000001318607,"domain_scores_codex":[0.9989969,0.00002017532,0.0004511463,0.000187633,0.0001365668,0.0002075391],"domain_scores_gemma":[0.9993233,0.0002254013,0.00008293469,0.0002892016,0.00004979883,0.00002936081],"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":[0.000003325986,0.00001690041,0.007570577,0.000181767,0.000006657052,0.000001269652,0.00006483729,0.9730247,0.005478389,0.0002351092,0.000001526786,0.01341497],"study_design_scores_gemma":[0.0002508732,0.00003422605,0.006866418,0.0009253368,0.000002698894,0.000001744328,0.000003773148,0.9723288,0.01901725,0.00004905719,0.0003481277,0.0001716917],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1261728,0.001025298,0.8718035,0.000001396456,0.0006057191,0.0001549723,0.000001491704,0.0001972497,0.00003764835],"genre_scores_gemma":[0.8380231,0.00003801895,0.1618024,0.000002497468,0.00008632321,0.00001791412,0.00000235277,0.00002735509,9.657866e-8],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7118503,"threshold_uncertainty_score":0.8197521,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007767132484739665,"score_gpt":0.234709401332244,"score_spread":0.2269422688475043,"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."}}