{"id":"W4376865759","doi":"10.1016/j.sna.2023.114429","title":"Optimization of MEMS-based Energy Scavengers and output prediction with machine learning and synthetic data approach","year":2023,"lang":"en","type":"article","venue":"Sensors and Actuators A Physical","topic":"Innovative Energy Harvesting Technologies","field":"Engineering","cited_by":25,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; Indian Institute of Technology Delhi; University of Alberta","keywords":"Power (physics); Context (archaeology); Computer science; Microelectromechanical systems; Energy (signal processing); Mechanical energy; Preprocessor; Data pre-processing; Efficient energy use; Artificial intelligence; Engineering; Materials science; Mathematics; Nanotechnology; Electrical engineering; Physics","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004512659,0.0005404588,0.000627444,0.0002793611,0.0002325187,0.0005262761,0.0005232973,0.0009494885,0.001550877],"category_scores_gemma":[0.0009662768,0.0003610794,0.0004246172,0.0002775341,0.0003317129,0.0005181353,0.0003281973,0.0004102744,0.0002064376],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004244262,"about_ca_system_score_gemma":0.000492169,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002165512,"about_ca_topic_score_gemma":0.002123003,"domain_scores_codex":[0.9998926,0.00002767206,0.000006396513,0.00002657741,0.00003117261,0.00001553541],"domain_scores_gemma":[0.9996388,0.000233824,0.00003418643,0.00002312054,0.00006206826,0.000007980245],"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":[0.00004019281,0.0000332126,0.0002318387,0.00005011343,0.00001969821,0.00001901589,0.000007208517,0.9817708,0.003574112,0.00102062,0.0002366001,0.01299658],"study_design_scores_gemma":[0.000002013009,0.000008555024,0.00005314415,0.000001094961,0.00000214895,0.000001696102,0.000001313608,0.9989622,0.000753991,0.0001576136,0.0000551736,0.000001052446],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1989036,0.0009071691,0.7907692,0.0005666728,0.0001041442,0.00009397119,0.000262273,0.0007756281,0.007617324],"genre_scores_gemma":[0.9493918,0.0001396158,0.04810479,0.00005568712,0.00002196531,0.000117387,0.0001140799,0.00003654434,0.002018047],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002165512,"threshold_uncertainty_score":0.005188227,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01544109918879528,"score_gpt":0.210374439517534,"score_spread":0.1949333403287387,"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."}}