{"id":"W1978249038","doi":"10.1115/1.3005165","title":"Effect of Calibration Method on Tekscan Sensor Accuracy","year":2008,"lang":"en","type":"article","venue":"Journal of Biomechanical Engineering","topic":"Knee injuries and reconstruction techniques","field":"Medicine","cited_by":173,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary; Vancouver Coastal Health; Vancouver Coastal Health Research Institute; University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada; Michael Smith Health Research BC","keywords":"Calibration; Range (aeronautics); Computer science; Accuracy and precision; Software; Artificial intelligence; Mathematics; Statistics; Engineering","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":[0.02119387,0.001673783,0.0008406105,0.002230229,0.0007882069,0.001944738,0.001930953,0.002287529,0.002275746],"category_scores_gemma":[0.1015589,0.001296683,0.0008168038,0.001969571,0.00170815,0.001444922,0.002072632,0.001206744,0.001201843],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008511372,"about_ca_system_score_gemma":0.000799797,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001595656,"about_ca_topic_score_gemma":0.001759439,"domain_scores_codex":[0.9668105,0.009576929,0.002680317,0.005142762,0.01507111,0.0007183242],"domain_scores_gemma":[0.9107935,0.05861685,0.004575243,0.008910476,0.01671229,0.0003916093],"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":[0.006121168,0.0005391074,0.06586928,0.00292114,0.0007012529,0.0006249557,0.003273857,0.03424251,0.4936955,0.003721789,0.006734127,0.3815553],"study_design_scores_gemma":[0.0001434093,0.002694187,0.04699815,0.0006465629,0.0004744261,0.001775372,0.0006152501,0.07419468,0.8527533,0.002092024,0.01726831,0.0003442012],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4691842,0.01066139,0.5021203,0.001498446,0.001621973,0.0008583703,0.0009927734,0.005470715,0.007591911],"genre_scores_gemma":[0.7429448,0.001996331,0.2475248,0.001310663,0.0001086275,0.0007092314,0.0008073457,0.001822985,0.002775181],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02119387,"threshold_uncertainty_score":0.1120853,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00982516026138277,"score_gpt":0.2967391504028583,"score_spread":0.2869139901414755,"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."}}