{"id":"W4210707136","doi":"10.1002/adfm.202112155","title":"Intelligent Sound Monitoring and Identification System Combining Triboelectric Nanogenerator‐Based Self‐Powered Sensor with Deep Learning Technique","year":2022,"lang":"en","type":"article","venue":"Advanced Functional Materials","topic":"Advanced Sensor and Energy Harvesting Materials","field":"Engineering","cited_by":81,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"National Natural Science Foundation of China","keywords":"Triboelectric effect; Software deployment; Wireless sensor network; Nanogenerator; Computer science; Deep learning; Identification (biology); Materials science; Systems engineering; Artificial intelligence; Electrical engineering; Engineering; Computer network","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0003954944,0.0002861988,0.00034135,0.0001975069,0.000589194,0.000124869,0.00009593449,0.00006240564,0.00008251388],"category_scores_gemma":[0.00004167054,0.0002963693,0.00003052635,0.0003111066,0.00002556967,0.0002193426,0.00004454721,0.0001729955,0.000008272668],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003677583,"about_ca_system_score_gemma":0.00002029344,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000006000874,"about_ca_topic_score_gemma":4.965149e-7,"domain_scores_codex":[0.9983087,0.0001789311,0.0004877701,0.0003787455,0.0003051986,0.0003407162],"domain_scores_gemma":[0.9993276,0.0001264221,0.0001769875,0.0001968445,0.00009052642,0.00008163742],"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":[0.0001059557,0.00001294614,0.00009237872,0.0001046264,0.00003005773,0.000008235132,0.00003724065,0.4004013,0.5987046,0.0002381219,0.000001293142,0.0002632252],"study_design_scores_gemma":[0.0005963703,0.0001762895,0.0004859648,0.0000605107,0.000040562,0.0001373872,0.0004074102,0.002823896,0.9942729,0.0001012279,0.0005080586,0.0003893988],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8902494,0.000251301,0.1061227,0.000004792124,0.001650351,0.0003850408,0.00002656753,0.001229435,0.00008035651],"genre_scores_gemma":[0.9897124,0.00003507929,0.00900524,0.000007625282,0.0001833786,0.0007758445,0.0001237902,0.00009961883,0.00005698392],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3975774,"threshold_uncertainty_score":0.9999489,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01019001689248751,"score_gpt":0.2052839802562349,"score_spread":0.1950939633637473,"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."}}