{"id":"W4315783969","doi":"10.1109/jsen.2023.3235198","title":"FEM-Inclusive Transfer Learning for Bistable Piezoelectric MEMS Energy Harvester Design","year":2023,"lang":"en","type":"article","venue":"IEEE Sensors Journal","topic":"Innovative Energy Harvesting Technologies","field":"Engineering","cited_by":19,"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; Canada Foundation for Innovation; CMC Microsystems","keywords":"Bistability; Finite element method; Artificial neural network; Microelectromechanical systems; Estimator; Computer science; Energy harvesting; Bandwidth (computing); Electronic engineering; Engineering; Energy (signal processing); Artificial intelligence; Materials science","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0006413346,0.0002861446,0.000300182,0.0007847543,0.0004333056,0.0002293018,0.0003006847,0.0001975988,0.00001980885],"category_scores_gemma":[0.0002889132,0.0002742456,0.0001252265,0.001388011,0.00006081281,0.0004109912,0.00002030722,0.0006654403,0.00002794144],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001985505,"about_ca_system_score_gemma":0.00005622851,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000007592344,"about_ca_topic_score_gemma":0.00000488056,"domain_scores_codex":[0.9982668,0.00008079282,0.0004085341,0.0002199393,0.0002568227,0.0007671468],"domain_scores_gemma":[0.9990916,0.0003758856,0.00005609664,0.0001656453,0.0002280514,0.00008271526],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00002717285,0.000008758153,0.00003636767,0.00002395534,0.000124591,0.00008885367,0.0001992563,0.8732906,0.09999752,0.001058067,0.01233671,0.01280818],"study_design_scores_gemma":[0.001229113,0.0003495737,0.0001484875,0.0001142764,0.00005162371,0.0004069619,0.0003106518,0.4001648,0.5219187,0.004313335,0.07019342,0.0007990471],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2334892,0.0001240838,0.7625628,0.0001839069,0.001034788,0.0001167757,0.000003966075,0.001783291,0.0007011758],"genre_scores_gemma":[0.9874267,0.0002540514,0.005125976,0.00004974879,0.0004709206,0.0000385663,0.000006469442,0.0001449368,0.006482604],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7574368,"threshold_uncertainty_score":0.999971,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02793513716463189,"score_gpt":0.2432762205403082,"score_spread":0.2153410833756763,"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."}}