{"id":"W2296757716","doi":"10.1109/lra.2016.2528301","title":"Mechanics of Tissue Cutting During Needle Insertion in Biological Tissue","year":2016,"lang":"en","type":"article","venue":"IEEE Robotics and Automation Letters","topic":"Soft Robotics and Applications","field":"Engineering","cited_by":71,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Alberta Innovates - Health Solutions","keywords":"Biomedical engineering; Soft tissue; Biological tissue; Materials science; Puncturing; Viscoelasticity; Displacement (psychology); Biomechanics; Anatomy; Composite material; Computer science; Surgery; Engineering; Medicine","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":[0.0001944168,0.0002255101,0.0002157,0.0001803471,0.0002565359,0.0004790759,0.0004388284,0.0007901731,0.0003582559],"category_scores_gemma":[0.0009182767,0.0002679068,0.0002093243,0.0001720596,0.0004890721,0.0004603838,0.0003535069,0.0003190064,0.0001948817],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004379181,"about_ca_system_score_gemma":0.0003308226,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001019825,"about_ca_topic_score_gemma":0.0009492736,"domain_scores_codex":[0.9997888,0.00002068425,0.000008235978,0.00004039157,0.0001132843,0.00002866574],"domain_scores_gemma":[0.9996823,0.0001362501,0.00008456629,0.00002591014,0.0000456665,0.00002527942],"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":[0.0001556482,0.0000688393,0.006173526,0.0001034953,0.00001463333,0.001165815,0.0005751715,0.1533346,0.8124772,0.00263931,0.0001804905,0.02311137],"study_design_scores_gemma":[0.00001814872,0.0004979182,0.01734922,0.0000241091,0.00001944278,0.001427733,0.0002408877,0.8212247,0.1555831,0.001598693,0.001948851,0.00006711741],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8490042,0.0009729494,0.147375,0.0001275688,0.00002194341,0.0000452016,0.00004438206,0.0001425692,0.002266204],"genre_scores_gemma":[0.9909107,0.000255787,0.007715924,0.00002946041,0.000004362485,0.00002009576,0.00002880465,0.00001470711,0.001020286],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001019825,"threshold_uncertainty_score":0.003177285,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01228958401451218,"score_gpt":0.2210423478244937,"score_spread":0.2087527638099815,"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."}}