{"id":"W2081292881","doi":"10.1016/j.jbiomech.2015.01.013","title":"Computed tomography landmark-based semi-automated mesh morphing and mapping techniques: Generation of patient specific models of the human pelvis without segmentation","year":2015,"lang":"en","type":"article","venue":"Journal of Biomechanics","topic":"Pelvic and Acetabular Injuries","field":"Medicine","cited_by":14,"is_retracted":false,"has_abstract":false,"ca_institutions":"Sunnybrook Health Science Centre; University of Toronto","funders":"Hospital for Sick Children; University of Toronto","keywords":"Morphing; Landmark; Segmentation; Computer science; Polygon mesh; Pelvis; Computer vision; Artificial intelligence; Finite element method; Computed tomography; Mesh generation; Anatomy; Radiology; Medicine; Computer graphics (images); Engineering; Structural 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004262477,0.0001011373,0.0002999978,0.0003116022,0.00004740019,0.00001477172,0.0000718805,0.00007151064,0.000002786269],"category_scores_gemma":[0.00001516319,0.00006866147,0.00009682233,0.0003199292,0.00004440801,0.0001178612,0.00002898405,0.0001144854,6.710916e-8],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004873995,"about_ca_system_score_gemma":0.00007858585,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000009458485,"about_ca_topic_score_gemma":0.000001092462,"domain_scores_codex":[0.9988013,0.00006843345,0.0005459444,0.00008494422,0.0004117637,0.00008759509],"domain_scores_gemma":[0.9986021,0.00001268852,0.000740041,0.0001363067,0.0004394051,0.00006946021],"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":[0.00008176513,0.0001353483,0.00116749,0.0001025118,0.00009257746,0.00000491377,0.001040813,0.000178833,0.9919089,0.000109376,0.0008664386,0.004311004],"study_design_scores_gemma":[0.001331951,0.0005906199,0.0001659027,0.0005377362,0.00010201,0.00006611617,0.000642857,0.2328296,0.7630574,0.0004247479,0.0001657459,0.00008536693],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9358916,0.0005702182,0.06298882,0.000136587,0.0001213346,0.0002289502,0.00001073812,0.00002651592,0.00002521152],"genre_scores_gemma":[0.9858772,0.00003618078,0.01390915,0.00008153466,0.00006502529,0.000001377916,0.00001496635,0.00001102964,0.000003601524],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2326507,"threshold_uncertainty_score":0.2799933,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04188329007239274,"score_gpt":0.2758387770991321,"score_spread":0.2339554870267393,"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."}}