{"id":"W6989628152","doi":"","title":"Calibrating surgical SmartForcepsTM using bootstrap and multilevel modeling techniques","year":2017,"lang":"en","type":"dissertation","venue":"Mspace (University of Manitoba)","topic":"Soft Robotics and Applications","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Manitoba","funders":"Natural Sciences and Engineering Research Council of Canada; University of Manitoba; University of Calgary","keywords":"Voltage; Point (geometry); Set (abstract data type); Bayesian probability; Probabilistic logic; Residual; Point estimation; Calibration; Prediction interval; Regression","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":[0.003745997,0.0007371925,0.0007486783,0.000998792,0.0004893086,0.0008691727,0.001361989,0.001126494,0.001259213],"category_scores_gemma":[0.01314919,0.0006888237,0.001199303,0.0008020473,0.0004215836,0.0009831749,0.001230314,0.00155015,0.000533747],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006430369,"about_ca_system_score_gemma":0.0008908326,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005930015,"about_ca_topic_score_gemma":0.007675925,"domain_scores_codex":[0.9986615,0.0004489506,0.00008905603,0.0002884648,0.0004295433,0.00008250865],"domain_scores_gemma":[0.9948564,0.003022793,0.0005662196,0.0006643804,0.000821842,0.00006837813],"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":[0.0001448417,0.0001292476,0.01070472,0.0001746943,0.0002125619,0.00009370227,0.0003484243,0.7966453,0.01315483,0.008072028,0.001070742,0.169249],"study_design_scores_gemma":[0.00000455609,0.00003864983,0.00138391,0.00001011372,0.000009541731,0.00001561804,0.00001774571,0.9942827,0.002035706,0.001757322,0.0004322871,0.00001185545],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.04297486,0.00006552004,0.9557436,0.000074269,0.00001265005,0.0000435072,0.00009597403,0.0004932662,0.0004964304],"genre_scores_gemma":[0.4574687,0.000111789,0.5408596,0.00006125977,0.00001801759,0.0002173855,0.0004750995,0.0002064364,0.0005817384],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.005930015,"threshold_uncertainty_score":0.01981097,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03938060655723924,"score_gpt":0.2489993782773956,"score_spread":0.2096187717201563,"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."}}