{"id":"W2548128663","doi":"10.1109/ccece.2016.7726622","title":"Energy harvesting for IoT sensors utilizing MEMS technology","year":2016,"lang":"en","type":"article","venue":"","topic":"Innovative Energy Harvesting Technologies","field":"Engineering","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Windsor","funders":"","keywords":"Transducer; Energy harvesting; Microelectromechanical systems; Electrical engineering; Voltage; USable; Cadence; Actuator; Efficient energy use; Diode; Power (physics); Electric potential energy; Computer science; Electronic circuit; Energy (signal processing); Electronics; Electronic engineering; Smart transducer; Engineering; Materials science; Optoelectronics; Physics","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":[0.00009192374,0.0001923859,0.0001719477,0.00018551,0.0002034179,0.0002826448,0.0002516259,0.0002855675,0.001973769],"category_scores_gemma":[0.0001308022,0.0001501934,0.0002036004,0.0002356862,0.0001442009,0.0004738367,0.0002429705,0.000265046,0.0006781714],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001729983,"about_ca_system_score_gemma":0.0001526909,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000104926,"about_ca_topic_score_gemma":0.0003156878,"domain_scores_codex":[0.9999212,0.000006390867,0.000004008619,0.00001050185,0.0000515281,0.000006360769],"domain_scores_gemma":[0.9999623,0.00001459974,0.000005133052,0.000004669656,0.00001087952,0.000002388475],"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":[0.00003280928,0.00001579343,0.0003732175,0.000191684,0.00001267566,0.0001297512,0.00004259252,0.001904954,0.9262218,0.008844936,0.001439841,0.06079001],"study_design_scores_gemma":[0.00002511811,0.0003747696,0.002840412,0.0001043833,0.00005721347,0.001228347,0.00009133128,0.08647794,0.7993766,0.01073875,0.09864656,0.00003855959],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2341663,0.01840871,0.6762984,0.001635277,0.0009101668,0.0001821902,0.0003013989,0.001545681,0.06655193],"genre_scores_gemma":[0.7853874,0.007878895,0.1867041,0.0004169194,0.0001973565,0.0001370951,0.0001557805,0.0001023385,0.01902007],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001973769,"threshold_uncertainty_score":0.006602943,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02200177687272605,"score_gpt":0.2296427451218885,"score_spread":0.2076409682491624,"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."}}