{"id":"W4406166658","doi":"10.1002/adsr.202400156","title":"Machine Learning‐Enabled Triboelectric Nanogenerator for Continuous Sound Monitoring and Captioning","year":2025,"lang":"en","type":"article","venue":"Advanced Sensor Research","topic":"Advanced Sensor and Energy Harvesting Materials","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"National Research Council Canada; University of Waterloo","funders":"","keywords":"Triboelectric effect; Nanogenerator; Closed captioning; Sound (geography); Computer science; Engineering; Artificial intelligence; Electrical engineering; Acoustics; Materials science; Physics; Voltage","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":[0.0005751656,0.0002236286,0.0003566055,0.0003798087,0.000510729,0.0001457624,0.0001377853,0.0001115772,0.00001079024],"category_scores_gemma":[0.0009643648,0.0002305749,0.00004954589,0.0006039137,0.00008293952,0.0001836435,0.0000525175,0.0004008283,0.000007470951],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001460377,"about_ca_system_score_gemma":0.0000255274,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003237472,"about_ca_topic_score_gemma":0.000005999441,"domain_scores_codex":[0.9981648,0.0001276978,0.0003170238,0.0003854045,0.0002225777,0.0007824484],"domain_scores_gemma":[0.9986265,0.0007314674,0.00003228458,0.0002197846,0.0002732975,0.0001166809],"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":[0.000121083,0.00001401809,0.001036191,0.0002156833,0.00005646337,0.00001093076,0.00008752858,0.1261655,0.8569298,0.001205199,0.00004769515,0.01410991],"study_design_scores_gemma":[0.002538451,0.0002289782,0.0003933204,0.0002016337,0.00002860507,0.00001851031,0.00033823,0.03018819,0.9317811,0.005351576,0.0283974,0.0005340679],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9828351,0.003857926,0.01022803,0.00004769033,0.0005189615,0.0005083145,0.00001586731,0.0005050005,0.001483121],"genre_scores_gemma":[0.986041,0.000770017,0.007956536,0.000007872207,0.0002040941,0.0001790973,0.00002076587,0.00007344743,0.004747146],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.09597729,"threshold_uncertainty_score":0.9402571,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03086855732736985,"score_gpt":0.3246112009874292,"score_spread":0.2937426436600593,"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."}}