{"id":"W2078400897","doi":"10.1115/1.1894372","title":"Biomechanical Study Using Fuzzy Systems to Quantify Collagen Fiber Recruitment and Predict Creep of the Rabbit Medial Collateral Ligament","year":2004,"lang":"en","type":"article","venue":"Journal of Biomechanical Engineering","topic":"Tendon Structure and Treatment","field":"Medicine","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia; Arthritis Research Centre of Canada; University of Calgary","funders":"Canadian Institutes of Health Research; Oak Ridge Associated Universities","keywords":"Creep; Materials science; Ligament; Medial collateral ligament; Fiber; Structural engineering; Computer science; Crimp; Work (physics); Biomedical engineering; Composite material; Mechanical engineering; Anatomy; Engineering; Biology","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":[0.0007122685,0.0003000243,0.0002117462,0.0004276221,0.0002056142,0.0002445639,0.0001992312,0.0004668122,0.0002285625],"category_scores_gemma":[0.001667973,0.0001712617,0.0003434956,0.000145543,0.0002306907,0.0002548881,0.0001355196,0.0001851089,0.00003645023],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004766467,"about_ca_system_score_gemma":0.0003286779,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004711985,"about_ca_topic_score_gemma":0.00523164,"domain_scores_codex":[0.9998802,0.00003605707,0.00001078255,0.00002210742,0.00003898878,0.00001195361],"domain_scores_gemma":[0.9992725,0.0004072152,0.0001032229,0.00004935918,0.0001332866,0.0000342948],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.000767027,0.0004293782,0.03503294,0.0001339914,0.00008929759,0.0002772953,0.0003239085,0.6554915,0.2565891,0.0008335076,0.0001023289,0.04992978],"study_design_scores_gemma":[0.000008597201,0.0003242076,0.004618919,0.0000047064,0.00001766004,0.00003624641,0.00002606581,0.9780917,0.01668585,0.0001216201,0.00005487826,0.000009526984],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9691043,0.00006520084,0.03049524,0.00003656476,0.000003846917,0.00002065082,0.00001479777,0.00003238659,0.0002270192],"genre_scores_gemma":[0.9903513,0.00003228226,0.009452448,0.000005063699,0.000001254645,0.00001414626,0.000009890103,0.000001422161,0.0001321949],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004711985,"threshold_uncertainty_score":0.009369135,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04619724657706028,"score_gpt":0.3015329766994673,"score_spread":0.2553357301224071,"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."}}