{"id":"W4200291572","doi":"10.1109/mass52906.2021.00019","title":"Entropic Sensing for Energy Efficiency","year":2021,"lang":"en","type":"article","venue":"","topic":"Distributed Sensor Networks and Detection Algorithms","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"","keywords":"Computer science; Wireless sensor network; Energy consumption; Entropy (arrow of time); Real-time computing; Efficient energy use; Wireless; Energy (signal processing); Algorithm; Mathematics; Statistics; Telecommunications; Computer network; Electrical engineering; Engineering","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":[0.001112652,0.0008005541,0.0007849289,0.0006404516,0.0004065584,0.001188398,0.001215677,0.0009013914,0.002138734],"category_scores_gemma":[0.005665662,0.0003437711,0.0004926078,0.0005775289,0.00196312,0.00228368,0.001680829,0.00165868,0.0003444908],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001106462,"about_ca_system_score_gemma":0.0005544025,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007071347,"about_ca_topic_score_gemma":0.000615198,"domain_scores_codex":[0.9989587,0.0002842573,0.00004317028,0.0001724341,0.0004647722,0.000076569],"domain_scores_gemma":[0.9978079,0.001473955,0.0001191652,0.0003768759,0.0001781409,0.0000439488],"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":[0.0001612822,0.00007648754,0.000458253,0.0002789879,0.00008604917,0.000171134,0.0001571369,0.4492892,0.03393502,0.4154608,0.002391551,0.09753416],"study_design_scores_gemma":[0.0000109715,0.00004599625,0.0001898716,0.00002242146,0.00001013723,0.00007903729,0.00001341989,0.8868674,0.007204289,0.102656,0.002875997,0.00002448741],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01128733,0.001536029,0.9776929,0.000750129,0.0001414383,0.00003720073,0.00004220601,0.0002489645,0.008263778],"genre_scores_gemma":[0.8113429,0.001436958,0.180234,0.000558414,0.0002750731,0.0001474477,0.00006528119,0.0001630778,0.00577689],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002138734,"threshold_uncertainty_score":0.008027911,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009698622333932197,"score_gpt":0.2167448308828744,"score_spread":0.2070462085489422,"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."}}