{"id":"W4402079519","doi":"10.1021/acsaelm.4c00841","title":"Screen-Printed Capacitive Tactile Sensor for Monitoring Tool–Tissue Interactions and Grasping Performances of a Surgical Magnetic Microgripper","year":2024,"lang":"en","type":"article","venue":"ACS Applied Electronic Materials","topic":"Advanced Sensor and Energy Harvesting Materials","field":"Engineering","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Rehabilitation Institute; University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research","keywords":"Capacitive sensing; Tactile sensor; Miniaturization; Materials science; Biomedical engineering; Microscale chemistry; Computer science; Robot; Nanotechnology; Artificial intelligence; Engineering","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0001782055,0.0002403602,0.0003684213,0.0001155311,0.00008254647,0.0001347924,0.00008629644,0.0000839978,0.0001026057],"category_scores_gemma":[0.00001275357,0.000230934,0.00003124286,0.000115193,0.00005727557,0.0001615439,0.00003176641,0.0001178108,0.00001022939],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000812036,"about_ca_system_score_gemma":0.00002077325,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003382515,"about_ca_topic_score_gemma":0.000004709695,"domain_scores_codex":[0.9987291,0.00001838374,0.0004053366,0.0002728403,0.00008780574,0.0004865192],"domain_scores_gemma":[0.9995773,0.0001549057,0.00004596626,0.0001434278,0.00003482167,0.00004359152],"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":[0.00006808189,0.000007354595,0.000003701424,0.0004235916,0.0000656683,0.000003114421,0.0001548788,0.001173296,0.9904306,0.003747167,0.00003402326,0.003888552],"study_design_scores_gemma":[0.0002800736,0.00008636885,0.00005346366,0.0001603788,0.00004895264,0.00006078427,0.0001114419,0.0001400164,0.988019,0.0005017355,0.01029179,0.0002459305],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9961391,0.0009108959,0.001288883,0.00001909169,0.000599297,0.0003439833,0.00006721743,0.0003274269,0.000304111],"genre_scores_gemma":[0.9975381,0.0008486741,0.0007742837,0.00000348927,0.0004017054,0.0001833075,0.00003626397,0.00006762063,0.0001465098],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01025777,"threshold_uncertainty_score":0.9417216,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008575850602608882,"score_gpt":0.2454208755632976,"score_spread":0.2368450249606887,"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."}}