{"id":"W2992081669","doi":"10.3390/nano9121737","title":"A Skin-Inspired Stretchable, Self-Healing and Electro-Conductive Hydrogel with a Synergistic Triple Network for Wearable Strain Sensors Applied in Human-Motion Detection","year":2019,"lang":"en","type":"article","venue":"Nanomaterials","topic":"Advanced Sensor and Energy Harvesting Materials","field":"Engineering","cited_by":109,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of New Brunswick","funders":"National Natural Science Foundation of China","keywords":"Self-healing; Wearable computer; Materials science; Human motion; Electrical conductor; Strain (injury); Nanotechnology; Wearable technology; Biomedical engineering; Self-healing hydrogels; Composite material; Computer science; Motion (physics); Embedded system; Engineering; Artificial intelligence; Polymer chemistry; Medicine; Anatomy","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":[0.0003614748,0.0002817613,0.0005036852,0.000114566,0.0001121032,0.00008687627,0.00006525345,0.0001614213,0.00002148709],"category_scores_gemma":[0.00001774499,0.000275376,0.00002856821,0.0001843192,0.00002216705,0.0001755561,0.00001267498,0.0000680789,0.000005225249],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001337185,"about_ca_system_score_gemma":0.00001527011,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000777631,"about_ca_topic_score_gemma":0.0000605955,"domain_scores_codex":[0.9985272,0.00004779361,0.0004212358,0.0003697604,0.0001053436,0.0005286438],"domain_scores_gemma":[0.9995226,0.00008003141,0.000118248,0.000182174,0.00003316744,0.00006377231],"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":[0.0001641653,0.00001418365,0.00002898129,0.0002218216,0.00003658425,0.000002112023,0.0001450459,0.1947385,0.8040082,0.0003211498,0.000003112013,0.0003161057],"study_design_scores_gemma":[0.002388806,0.0002964709,0.0003872725,0.0001526004,0.00004728702,0.0000175922,0.0001153141,0.01826379,0.9757813,0.001904249,0.0001534696,0.000491841],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9948695,0.00005369895,0.003024883,0.000002669952,0.000445459,0.0008738036,0.00004012926,0.0004283383,0.0002615676],"genre_scores_gemma":[0.995243,0.00001662668,0.004134151,0.000009312439,0.0002193338,0.0001900411,0.00004668226,0.00008733114,0.00005350217],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1764747,"threshold_uncertainty_score":0.9999698,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007154834394895748,"score_gpt":0.20567505011753,"score_spread":0.1985202157226343,"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."}}