{"id":"W1997724340","doi":"10.1109/haptics.2008.4479924","title":"Finger Shell: Predicting Finger Pad Deformation under Line Loading","year":2008,"lang":"en","type":"article","venue":"","topic":"Robot Manipulation and Learning","field":"Engineering","cited_by":15,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Stiffness; Deflection (physics); Computer science; Deformation (meteorology); Shell (structure); Bending; Line (geometry); Structural engineering; Algorithm; Mechanical engineering; Materials science; Engineering; Mathematics; Geometry; Composite material; Physics; Optics","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":[],"consensus_categories":[],"category_scores_codex":[0.0001054386,0.0001131099,0.0001012604,0.0001002656,0.0001585552,0.00002875737,0.00005730902,0.00007017925,0.0006494533],"category_scores_gemma":[0.0000310112,0.0001079639,0.00004173141,0.0001559011,0.00001138183,0.0003369362,0.00001659948,0.0001892614,0.0002994864],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005419733,"about_ca_system_score_gemma":0.000006890641,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001412688,"about_ca_topic_score_gemma":0.00000720942,"domain_scores_codex":[0.9993125,0.00001360077,0.0002313873,0.0001003833,0.0001383859,0.0002037174],"domain_scores_gemma":[0.9997333,0.00004650197,0.00002932667,0.000108803,0.00002766373,0.00005440468],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000001352074,0.000004899303,0.005891772,0.00001926214,0.00001295582,0.000002023053,0.0006556868,0.9895976,0.001638959,0.0002594856,0.0007341147,0.00118192],"study_design_scores_gemma":[0.0001807325,0.00001068688,0.01663347,0.0000244466,0.00000534442,0.00002238766,0.0001190057,0.9791837,0.001581477,0.00002877968,0.002053602,0.000156316],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.506691,0.0001091889,0.4437079,0.0001670239,0.0003986908,0.0001125201,2.015992e-7,0.001314108,0.04749933],"genre_scores_gemma":[0.9958124,0.00002456192,0.002105177,0.0001501025,0.0001892876,0.000004439909,0.00001421303,0.00002962959,0.001670192],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4891214,"threshold_uncertainty_score":0.7111059,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03383394528102022,"score_gpt":0.2218148394703765,"score_spread":0.1879808941893563,"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."}}