{"id":"W2552713324","doi":"10.1109/mpot.2016.2549998","title":"Energy Harvesting From the Human Body for Biomedical Applications","year":2016,"lang":"en","type":"article","venue":"IEEE Potentials","topic":"Innovative Energy Harvesting Technologies","field":"Engineering","cited_by":37,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Energy harvesting; Energy (signal processing); Electric potential energy; Thermal energy; Energy source; Electrical engineering; Available energy; Renewable energy; Wind power; Engineering; Computer science; Environmental science; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001951227,0.0001414782,0.0001450482,0.00006550069,0.0002005183,0.00004249036,0.0004427955,0.0001171241,0.00002819454],"category_scores_gemma":[0.0001540685,0.00008530118,0.00005565416,0.0002069659,0.0001868053,0.00009356025,0.0000456511,0.00006806359,0.000022871],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003950189,"about_ca_system_score_gemma":0.00001191863,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001028186,"about_ca_topic_score_gemma":0.00002018975,"domain_scores_codex":[0.9990944,0.00002115519,0.0002819076,0.0002022673,0.0001277541,0.0002724912],"domain_scores_gemma":[0.9990105,0.0004293559,0.00006226757,0.0003942473,0.00007206692,0.0000315484],"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":[8.880731e-7,0.00001313808,0.00006986845,0.000005897833,0.00006329614,9.180983e-7,0.00001018744,0.00006940819,0.8841994,0.03456527,0.01110056,0.06990114],"study_design_scores_gemma":[0.0005170346,0.00002473669,0.001484154,0.0001027767,0.0000395042,0.000003349439,0.00002563508,0.00101256,0.6496797,0.06515418,0.2816034,0.0003530079],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05167856,0.00009117456,0.94499,0.0007645305,0.000406341,0.0001600302,0.0001262957,0.001266213,0.0005168945],"genre_scores_gemma":[0.9896453,0.00001200167,0.008520194,0.00007758356,0.0006605259,0.000487116,0.00003264388,0.0000449909,0.000519646],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9379668,"threshold_uncertainty_score":0.3478481,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02153640742862746,"score_gpt":0.2514412210908385,"score_spread":0.229904813662211,"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."}}