{"id":"W2582734402","doi":"10.1109/iccs.2016.7833606","title":"Deploying autonomous sensors in a substation area using energy harvesting and wireless transfer of energy","year":2016,"lang":"en","type":"article","venue":"","topic":"Energy Harvesting in Wireless Networks","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Wireless sensor network; Energy harvesting; Reliability (semiconductor); Wireless; Electrical engineering; Voltage; Energy (signal processing); Computer science; Wireless power transfer; Key distribution in wireless sensor networks; Maximum power transfer theorem; Power (physics); Electronic engineering; Engineering; Wireless network; Telecommunications; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001517842,0.0002886719,0.0002798591,0.0001536223,0.0002892287,0.0003799972,0.0004125398,0.0004701414,0.0003611055],"category_scores_gemma":[0.0002219753,0.0001806834,0.0002702593,0.0002875589,0.0005025968,0.001003809,0.0004745518,0.0002103092,0.0001174909],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001715549,"about_ca_system_score_gemma":0.0001656788,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004648379,"about_ca_topic_score_gemma":0.0006472194,"domain_scores_codex":[0.9998665,0.00003806442,0.000005085528,0.00004099961,0.00003549033,0.00001374295],"domain_scores_gemma":[0.9998879,0.00004413005,0.00002464497,0.00002336572,0.00001217259,0.000007737842],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0001396415,0.0003401589,0.00655747,0.0003352281,0.00009299612,0.0007850977,0.0003855417,0.3497231,0.4760762,0.02437744,0.001279896,0.1399072],"study_design_scores_gemma":[0.00002312859,0.0004672993,0.003466759,0.00001711336,0.00004961349,0.0004038615,0.0002623765,0.9284061,0.04900204,0.01207816,0.005796119,0.00002744902],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2071024,0.0008111977,0.7874859,0.0002923381,0.00005795679,0.00007987343,0.00003673195,0.0002124006,0.003921119],"genre_scores_gemma":[0.9376808,0.0008213563,0.05817553,0.00004709544,0.00004090872,0.00007858883,0.00003018846,0.00001377503,0.003111652],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0004701414,"threshold_uncertainty_score":0.001244724,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01602429294520877,"score_gpt":0.195157365758364,"score_spread":0.1791330728131553,"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."}}