{"id":"W2140556288","doi":"10.1109/tpds.2011.180","title":"Leveraging Prediction to Improve the Coverage of Wireless Sensor Networks","year":2011,"lang":"en","type":"article","venue":"IEEE Transactions on Parallel and Distributed Systems","topic":"Energy Efficient Wireless Sensor Networks","field":"Computer Science","cited_by":52,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Wireless sensor network; Submodular set function; Leverage (statistics); Greedy algorithm; Node (physics); Entropy (arrow of time); Aggregate (composite); Distributed computing; Data mining; Mathematical optimization; Algorithm; Computer network; Machine learning","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":[0.001448613,0.0009578664,0.001296557,0.0005196342,0.0004867166,0.000580853,0.001522925,0.0009060333,0.0005295158],"category_scores_gemma":[0.007150104,0.0003627668,0.0004483575,0.001051952,0.0009118441,0.002170691,0.001832238,0.001058652,0.0001587125],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005682354,"about_ca_system_score_gemma":0.0007403443,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001659446,"about_ca_topic_score_gemma":0.002003837,"domain_scores_codex":[0.9989349,0.0003430397,0.00004555504,0.0002147719,0.0003416413,0.0001200586],"domain_scores_gemma":[0.9960275,0.002576206,0.0004775558,0.0005284559,0.0002933003,0.0000970516],"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":[0.0001236258,0.00005333651,0.0008454464,0.00007124231,0.00003216571,0.00005963957,0.00005689995,0.9304708,0.006515958,0.003958315,0.0007587275,0.05705393],"study_design_scores_gemma":[0.000008842311,0.00004208762,0.0001331432,0.000002955046,0.000007726845,0.00002254299,0.000005911223,0.9946844,0.001434784,0.0035116,0.0001424452,0.000003508264],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.03440462,0.0004341782,0.9637291,0.0002446155,0.00003053326,0.000026212,0.00003178489,0.0003861462,0.000712725],"genre_scores_gemma":[0.8917787,0.0005140575,0.1066696,0.0001295339,0.00009750478,0.00007972032,0.00009833393,0.0000668602,0.0005657353],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.001659446,"threshold_uncertainty_score":0.007661104,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01921452962852331,"score_gpt":0.2047706582568655,"score_spread":0.1855561286283421,"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."}}