{"id":"W2920925260","doi":"10.1016/j.jmbbm.2019.03.009","title":"Experimental and finite element analyses of bone strains in the growing rat tibia induced by in vivo axial compression","year":2019,"lang":"en","type":"article","venue":"Journal of the mechanical behavior of biomedical materials/Journal of mechanical behavior of biomedical materials","topic":"Bone fractures and treatments","field":"Medicine","cited_by":13,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université de Montréal; Polytechnique Montréal; Centre Hospitalier Universitaire Sainte-Justine","funders":"Natural Sciences and Engineering Research Council of Canada; Centre de recherche du CHU Sainte-Justine","keywords":"Strain (injury); Finite element method; In vivo; Strain gauge; Tibia; Compression (physics); Materials science; Anatomy; Structural engineering; Biology; Composite material; 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":[0.0002917517,0.000393428,0.0002339174,0.0006168337,0.0002783389,0.0002109263,0.0005988973,0.0004558066,0.00335766],"category_scores_gemma":[0.0005500744,0.0003376986,0.0003278808,0.0004422421,0.0005814072,0.0001927838,0.000164569,0.0003232534,0.0002540525],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002560433,"about_ca_system_score_gemma":0.0003516883,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002346197,"about_ca_topic_score_gemma":0.003584149,"domain_scores_codex":[0.9997942,0.0000198073,0.00001705471,0.00002420819,0.0001144854,0.00003028103],"domain_scores_gemma":[0.9995247,0.0002109731,0.00009197379,0.00004788203,0.00009352603,0.00003090311],"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":[0.0005754006,0.0002398245,0.003299949,0.0001257576,0.00001593971,0.0001182974,0.000120698,0.02374719,0.963824,0.0005189782,0.00007740369,0.007336557],"study_design_scores_gemma":[0.00005212382,0.00081113,0.06142774,0.00002740264,0.00006398658,0.0003679678,0.0002622957,0.1065534,0.8291655,0.0002501254,0.0009840917,0.00003437104],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.992078,0.0001139052,0.005839435,0.0000217054,0.000008950372,0.00001989137,0.0003013682,0.00007296639,0.001543733],"genre_scores_gemma":[0.9956721,0.0001024957,0.002773623,0.000008143703,0.000003204898,0.00003748405,0.0002030049,0.00001933455,0.001180603],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.00335766,"threshold_uncertainty_score":0.01123244,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03329739505833075,"score_gpt":0.3358385706729103,"score_spread":0.3025411756145795,"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."}}