{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002212144,0.0003716126,0.0003081852,0.0002893393,0.0001372718,0.0003763899,0.000656048,0.0003746379,0.001761953],"category_scores_gemma":[0.0005967797,0.0001095088,0.0002056322,0.0002531882,0.0001671026,0.0004486071,0.000327718,0.0003196906,0.0006280497],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003435706,"about_ca_system_score_gemma":0.0002793419,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00103576,"about_ca_topic_score_gemma":0.001525262,"domain_scores_codex":[0.9998734,0.00001490309,0.00000672649,0.00004053391,0.00005134767,0.00001308969],"domain_scores_gemma":[0.9998264,0.00005332055,0.00002201584,0.00002560547,0.00006252231,0.00001011804],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.000509578,0.000158819,0.002452282,0.0003200855,0.00005369846,0.000370371,0.00009778595,0.09816278,0.4254794,0.002324329,0.005213342,0.4648576],"study_design_scores_gemma":[0.00000967177,0.000113187,0.0008325265,0.000009785918,0.00001482238,0.00007983085,0.00002090577,0.8414839,0.1530745,0.0008045015,0.003540062,0.00001634029],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2135253,0.0007932071,0.7678184,0.0004346544,0.0002948994,0.000149256,0.0006549412,0.009247482,0.007081965],"genre_scores_gemma":[0.8907772,0.0002365386,0.102762,0.0001816409,0.00003421773,0.0001011443,0.0005399058,0.0001208523,0.00524646],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001761953,"threshold_uncertainty_score":0.005894303,"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."}}