{"id":"W2595841077","doi":"10.1097/brs.0000000000002156","title":"Damage Identification on Vertebral Bodies During Compressive Loading Using Digital Image Correlation","year":2017,"lang":"en","type":"article","venue":"Spine","topic":"Bone health and osteoporosis research","field":"Medicine","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"International Collaboration On Repair Discoveries; University of British Columbia","funders":"","keywords":"Cadaveric spasm; Digital image correlation; Vertebra; Displacement (psychology); Fracture (geology); Compression (physics); Medicine; Biomedical engineering; Biomechanics; Strain (injury); Strain gauge; Ex vivo; Materials science; Anatomy; Composite material; In vivo","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":[0.0004189186,0.0002722797,0.0002308063,0.001268854,0.0002692426,0.0003163196,0.0003065267,0.0005099963,0.001311409],"category_scores_gemma":[0.001351171,0.0002621396,0.0001630853,0.0005680386,0.0006196547,0.0003846859,0.0004034704,0.0003442885,0.0002254699],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003802197,"about_ca_system_score_gemma":0.0003639254,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002472781,"about_ca_topic_score_gemma":0.005327173,"domain_scores_codex":[0.9997332,0.0000267602,0.00001331048,0.00004368222,0.000150387,0.00003269299],"domain_scores_gemma":[0.9993398,0.000196227,0.0001615575,0.00005169511,0.0002076917,0.00004313661],"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":[0.0003076772,0.00009566746,0.01459063,0.0002261019,0.00001837241,0.0002806572,0.0001994125,0.001188879,0.9339504,0.0001083878,0.0001573444,0.04887643],"study_design_scores_gemma":[0.00003151532,0.001930164,0.3605758,0.00006426476,0.0001031357,0.002931105,0.0004667963,0.02079715,0.6115415,0.0002018771,0.001310953,0.00004569357],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9754747,0.0007949421,0.0220728,0.00005007024,0.00001516294,0.00009991958,0.0001738078,0.00007049698,0.001248192],"genre_scores_gemma":[0.9782977,0.000515602,0.02003755,0.00005204496,0.00001160556,0.00005729348,0.0002331239,0.00001000021,0.0007851559],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002472781,"threshold_uncertainty_score":0.004916728,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04120393968616195,"score_gpt":0.3677861168943544,"score_spread":0.3265821772081925,"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."}}