{"id":"W2077318038","doi":"10.1109/twc.2012.121412.120148","title":"STCDG: An Efficient Data Gathering Algorithm Based on Matrix Completion for Wireless Sensor Networks","year":2012,"lang":"en","type":"article","venue":"IEEE Transactions on Wireless Communications","topic":"Sparse and Compressive Sensing Techniques","field":"Engineering","cited_by":133,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Prince Edward Island","funders":"","keywords":"Wireless sensor network; Computer science; Matrix completion; Compressed sensing; Network packet; Adaptability; Algorithm; Data collection; Data loss; Stability (learning theory); Set (abstract data type); Optimization problem; Real-time computing; Mathematics; Machine learning; Computer network","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0003066186,0.0002800233,0.0002689752,0.0001950776,0.0005796457,0.00007270234,0.001074082,0.0001534594,0.00001465571],"category_scores_gemma":[0.000002530428,0.000308523,0.0001010215,0.0002757324,0.0001038909,0.0002088895,0.00001174407,0.0004608174,0.00001519284],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001279021,"about_ca_system_score_gemma":0.00001874595,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004989874,"about_ca_topic_score_gemma":0.0000464289,"domain_scores_codex":[0.9986128,0.0001468542,0.0003367411,0.0002738523,0.0002017231,0.0004280523],"domain_scores_gemma":[0.9957719,0.0004253127,0.00006930459,0.003478834,0.00009792977,0.0001566468],"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":[0.00002341144,0.0006049654,0.000005818919,0.00001402113,0.00004892693,3.14847e-7,0.0001309737,0.9307221,0.002404088,0.0002231337,0.000197697,0.06562457],"study_design_scores_gemma":[0.0003565279,0.00006984128,0.0000638705,0.0001221796,0.0000704645,0.000004792734,0.00008503689,0.9895536,0.007191233,0.000007885318,0.002146222,0.0003284003],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01658425,0.0001243299,0.980373,0.0001302159,0.0004628884,0.0006083595,0.0003291735,0.001221864,0.0001658799],"genre_scores_gemma":[0.9249285,0.0001698742,0.0741436,0.00008378392,0.0000861321,0.0001920559,0.0002805894,0.00009975585,0.00001568186],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9083443,"threshold_uncertainty_score":0.9999367,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06246138348487786,"score_gpt":0.3076555059859662,"score_spread":0.2451941225010884,"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."}}