{"id":"W4409748653","doi":"10.1145/3706598.3713167","title":"IntelliLining: Activity Sensing through Textile Interlining Sensors Using TENGs","year":2025,"lang":"en","type":"article","venue":"","topic":"Interactive and Immersive Displays","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Calgary; Simon Fraser University","funders":"","keywords":"Interlining; Textile; Computer science; Engineering; Materials science; Aerospace engineering","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":[0.0001984306,0.0005016809,0.0002853732,0.0004422703,0.0001838338,0.0005371297,0.0005376816,0.0003792642,0.003497241],"category_scores_gemma":[0.0002948823,0.0002124686,0.0001545003,0.0004421553,0.0002524928,0.0007274089,0.0006622798,0.0002069948,0.0004250499],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001563937,"about_ca_system_score_gemma":0.0001313027,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004955612,"about_ca_topic_score_gemma":0.001910987,"domain_scores_codex":[0.9997867,0.00003346005,0.00001015565,0.00006343476,0.0000746924,0.00003158853],"domain_scores_gemma":[0.9998525,0.00004051753,0.00002777115,0.00001626434,0.00003997588,0.00002291055],"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":[0.0005090763,0.0001526477,0.005106054,0.0002515584,0.00002653144,0.0002753504,0.000496664,0.0009659809,0.8595122,0.0009028313,0.001495579,0.1303057],"study_design_scores_gemma":[0.00006834641,0.001592073,0.04606459,0.00009673695,0.0001073896,0.001626499,0.0007566063,0.04849782,0.8780233,0.001356757,0.0216883,0.0001215689],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7105365,0.0007940765,0.2711171,0.0002375297,0.0003862213,0.0001440008,0.0006941962,0.002139591,0.01395085],"genre_scores_gemma":[0.9335658,0.0004042512,0.05496681,0.0002615806,0.00003393329,0.00006343686,0.0002158054,0.0001150059,0.01037336],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003497241,"threshold_uncertainty_score":0.01169944,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03610933936446093,"score_gpt":0.3327289609566032,"score_spread":0.2966196215921423,"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."}}