{"id":"W2095822213","doi":"10.1109/iembs.2009.5335349","title":"Longitudinal strain estimation in incompressible cylindrical tissues from magnetic resonance imaging","year":2009,"lang":"en","type":"article","venue":"","topic":"Elasticity and Material Modeling","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"National Institute of Neurological Disorders and Stroke","keywords":"Deformation (meteorology); Magnetic resonance imaging; Compressibility; Noise (video); Constraint (computer-aided design); Soft tissue; Computer science; Physics; Medical imaging; Computer vision; Image (mathematics); Acoustics; Artificial intelligence; Geometry; Mathematics; Mechanics","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00004597252,0.00009449217,0.0001152803,0.00005746543,0.00002152311,0.00004986713,0.00007682378,0.00003802988,0.000143848],"category_scores_gemma":[0.00001413115,0.00009783987,0.00001291906,0.00007937098,0.00001034736,0.0001821137,0.00001227501,0.00009454221,0.00003309074],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003045009,"about_ca_system_score_gemma":0.00000470683,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001391831,"about_ca_topic_score_gemma":0.00005997202,"domain_scores_codex":[0.9994198,0.00001038569,0.0001819838,0.0001301318,0.00008517889,0.0001725374],"domain_scores_gemma":[0.9998294,0.00002917596,0.000008410872,0.00008913576,0.000009039728,0.00003488101],"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":[0.00002818394,0.00006249972,0.002984456,0.00002492702,0.000002315976,0.00005916995,0.00041635,0.6154567,0.04959134,0.002350298,0.000178356,0.3288454],"study_design_scores_gemma":[0.000231932,0.0000181544,0.07075904,0.00008410303,0.000003515383,0.000002020626,0.00001290549,0.9193063,0.003221079,0.006127132,0.0001085818,0.0001252839],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7551149,0.002424968,0.2362442,0.0001554502,0.0002085852,0.0001082205,0.00001377765,0.0003805418,0.005349375],"genre_scores_gemma":[0.9695408,0.00002289922,0.0303025,0.00003212211,0.00005407016,0.000003152328,0.00001160556,0.00000859504,0.00002425898],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3287201,"threshold_uncertainty_score":0.3989794,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01217944274444274,"score_gpt":0.2371312120391018,"score_spread":0.2249517692946591,"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."}}