{"id":"W2922366618","doi":"10.1109/jsen.2019.2904025","title":"Nonlinear Multi-Mode Wideband Piezoelectric MEMS Vibration Energy Harvester","year":2019,"lang":"en","type":"article","venue":"IEEE Sensors Journal","topic":"Innovative Energy Harvesting Technologies","field":"Engineering","cited_by":69,"is_retracted":false,"has_abstract":true,"ca_institutions":"Memorial University of Newfoundland","funders":"Natural Sciences and Engineering Research Council of Canada; Memorial University of Newfoundland; Research and Development Corporation of Newfoundland and Labrador; Canada Foundation for Innovation; CMC Microsystems","keywords":"Bandwidth (computing); Nonlinear system; Energy harvesting; Wideband; Microelectromechanical systems; Piezoelectricity; Voltage; Vibration; Proof mass; Power density; Computer science; Physics; Electrical engineering; Acoustics; Electronic engineering; Energy (signal processing); Power (physics); Engineering; Optoelectronics; Telecommunications","routes":{"ca_aff":true,"ca_fund":true,"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":[],"consensus_categories":[],"category_scores_codex":[0.0001818325,0.0002476796,0.0002455516,0.0004122554,0.0001012461,0.0002095134,0.0002515613,0.000189176,0.00004916808],"category_scores_gemma":[0.00008570244,0.0002264118,0.00007747387,0.00050717,0.00004230781,0.0006943496,0.00001852145,0.0005777442,0.00008322724],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001370953,"about_ca_system_score_gemma":0.00003315873,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001702006,"about_ca_topic_score_gemma":0.00002059117,"domain_scores_codex":[0.9987058,0.00004024255,0.0003973633,0.0001886463,0.0002455743,0.0004223666],"domain_scores_gemma":[0.99933,0.00006823733,0.0001160288,0.0002590749,0.0001595498,0.00006706086],"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":[0.00002270503,0.00005527598,0.001384591,0.00002837779,0.0001526111,0.00007337194,0.0001550342,0.3136643,0.6664132,0.0006592215,0.00433316,0.01305809],"study_design_scores_gemma":[0.0009687233,0.0001242914,0.0007122658,0.00007784452,0.00001661012,0.0004689082,0.00004901045,0.4064678,0.5764593,0.000335643,0.01380026,0.0005193743],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8431098,0.0001144961,0.1534476,0.0000926583,0.001577448,0.00005891456,0.000004045373,0.00057531,0.001019804],"genre_scores_gemma":[0.9804677,0.00009614762,0.01602202,0.0001093476,0.000388991,0.000003457875,0.000005404766,0.00007702395,0.0028299],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1374255,"threshold_uncertainty_score":0.9232804,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01406618463250711,"score_gpt":0.2319608504636277,"score_spread":0.2178946658311206,"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."}}