{"id":"W2131082250","doi":"10.1123/jab.22.2.120","title":"Response Surface Optimization for Joint Contact Model Evaluation","year":2006,"lang":"en","type":"article","venue":"Journal of Applied Biomechanics","topic":"Knee injuries and reconstruction techniques","field":"Medicine","cited_by":24,"is_retracted":false,"has_abstract":true,"ca_institutions":"Biorem Technologies (Canada)","funders":"U.S. National Library of Medicine; National Institutes of Health","keywords":"Joint (building); Contact area; Contact force; Range (aeronautics); Finite element method; Position (finance); Response surface methodology; Contact analysis; Computer science; Materials science; Structural engineering; Engineering; Physics","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":[0.004236459,0.001775207,0.001653476,0.001286124,0.0004955917,0.0008804142,0.0009922553,0.001379729,0.004357992],"category_scores_gemma":[0.008042849,0.0008657661,0.001432464,0.0009476804,0.0006107076,0.0007079567,0.001043339,0.001368155,0.001221292],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007263477,"about_ca_system_score_gemma":0.00111834,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003395965,"about_ca_topic_score_gemma":0.002168278,"domain_scores_codex":[0.9979382,0.001048248,0.000103139,0.0001686561,0.0006472793,0.00009442369],"domain_scores_gemma":[0.9969673,0.001955928,0.0001710448,0.0002966447,0.0005730375,0.00003613418],"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":[0.0001196477,0.0001142877,0.0006591226,0.0002742734,0.00009059155,0.0000805877,0.00009472795,0.908649,0.01206893,0.00535119,0.001201768,0.07129586],"study_design_scores_gemma":[0.0000107673,0.00005018728,0.0001575843,0.00000826336,0.000006641709,0.0000138136,0.00001352672,0.9950122,0.002458887,0.00108987,0.001166911,0.00001132373],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.007685261,0.0001124918,0.9898064,0.00005585764,0.00001371405,0.0001144858,0.00007198281,0.00103057,0.001109242],"genre_scores_gemma":[0.2798679,0.0003270671,0.7142471,0.00008893474,0.00002529786,0.001638977,0.0005719858,0.0008888358,0.002343939],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004357992,"threshold_uncertainty_score":0.02240479,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02386910769180096,"score_gpt":0.2878568785385099,"score_spread":0.2639877708467089,"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."}}