{"id":"W4401888856","doi":"10.3390/app14167354","title":"Development of an Adaptive Force Control Strategy for Soft Robotic Gripping","year":2024,"lang":"en","type":"article","venue":"Applied Sciences","topic":"Soft Robotics and Applications","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of New Brunswick","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002394609,0.0004655715,0.0003342298,0.0003325656,0.0002232803,0.0003936249,0.0007392492,0.0005150474,0.00132108],"category_scores_gemma":[0.0003828505,0.0001857408,0.000315708,0.0001499607,0.000336189,0.0003508302,0.0004577507,0.0004785843,0.0002776336],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002312728,"about_ca_system_score_gemma":0.0004161147,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002649747,"about_ca_topic_score_gemma":0.001949104,"domain_scores_codex":[0.9998394,0.00001514386,0.00001050961,0.00004305367,0.00007536796,0.00001658026],"domain_scores_gemma":[0.9998172,0.00004133751,0.0000349153,0.00001643991,0.00007811737,0.0000120748],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0001324533,0.0001227202,0.0004846742,0.0003156799,0.00005813575,0.0004590954,0.0002566735,0.386172,0.246408,0.01957033,0.001526437,0.3444939],"study_design_scores_gemma":[0.00001141017,0.0001475114,0.0002132968,0.00001173632,0.000009262151,0.00005810316,0.00001162769,0.9860664,0.0103673,0.001027223,0.002063183,0.00001296134],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01581576,0.0001845186,0.979388,0.00006920114,0.00006142316,0.00006092883,0.000007928069,0.00028073,0.00413144],"genre_scores_gemma":[0.8331659,0.0002387728,0.1606239,0.0001351713,0.00004265592,0.0001908369,0.00003047522,0.00003268516,0.005539597],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002649747,"threshold_uncertainty_score":0.005268574,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04135298603854901,"score_gpt":0.2720193189336463,"score_spread":0.2306663328950973,"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."}}