{"id":"W3139139506","doi":"10.48550/arxiv.2103.11116","title":"Multi-Axis Force Sensing in Robotic Minimally Invasive Surgery With No Instrument Modification","year":2021,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Soft Robotics and Applications","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Surgical instrument; Calibration; SIGNAL (programming language); Tube (container); Stiffness; Bending; Instrumentation (computer programming); Robot; Invasive surgery; Engineering; Acoustics; Simulation; Computer science; Mechanical engineering; Structural engineering; Artificial intelligence; Physics; Surgery","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004835132,0.0004626164,0.0003111378,0.0002938005,0.0001607223,0.0003653394,0.0007049462,0.0007588223,0.0006768726],"category_scores_gemma":[0.0009185401,0.0003501738,0.0002911413,0.0002248494,0.0005071563,0.000818743,0.0005915017,0.0004341108,0.000222536],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002650296,"about_ca_system_score_gemma":0.0001887307,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005233854,"about_ca_topic_score_gemma":0.000853791,"domain_scores_codex":[0.9994503,0.0000967943,0.00002449898,0.0001336647,0.0002671695,0.00002760306],"domain_scores_gemma":[0.9996046,0.0001656793,0.00008498529,0.00006369349,0.0000643091,0.00001675085],"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":[0.0004067342,0.00009209369,0.002517302,0.0005047629,0.00005641317,0.0002362706,0.0002030039,0.07419069,0.5175718,0.008113602,0.0008253347,0.3952821],"study_design_scores_gemma":[0.00002875117,0.0008413583,0.007124825,0.00006868337,0.00004520077,0.0009064793,0.00004454084,0.8144035,0.1590487,0.006305801,0.01107937,0.0001028301],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04166365,0.002269864,0.95294,0.0001871786,0.0001071514,0.00004497237,0.00003591194,0.0004239699,0.00232732],"genre_scores_gemma":[0.6971273,0.0009826176,0.2988002,0.0001753624,0.00009548566,0.00005763898,0.00003832311,0.00004309486,0.002679926],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0007588223,"threshold_uncertainty_score":0.002557099,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07829988329789869,"score_gpt":0.1789720885273344,"score_spread":0.1006722052294357,"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."}}