{"id":"W2315700516","doi":"10.1177/016173460202400204","title":"Analysis of an Adaptive Strain Estimation Technique in Elastography","year":2002,"lang":"en","type":"article","venue":"Ultrasonic Imaging","topic":"Ultrasound Imaging and Elastography","field":"Medicine","cited_by":57,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Cancer Institute; Ryerson University","keywords":"Elastography; Decorrelation; SIGNAL (programming language); Strain (injury); Displacement (psychology); Signal processing; Noise (video); Materials science; Lateral strain; Computer science; Acoustics; Mathematics; Algorithm; Artificial intelligence; Physics; Ultrasound; Composite material; Digital signal processing","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004459103,0.0001917457,0.0004494347,0.002140634,0.00005021298,0.0000255292,0.0001346358,0.00006206522,0.0002302998],"category_scores_gemma":[0.00009441534,0.0001903688,0.0002824994,0.003299663,0.0001713362,0.0002884958,0.00001089331,0.0003156433,0.000005230826],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005340013,"about_ca_system_score_gemma":0.00002191628,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003484273,"about_ca_topic_score_gemma":0.00005534614,"domain_scores_codex":[0.9984633,0.00007451456,0.0004017707,0.0003581325,0.0003559756,0.0003462647],"domain_scores_gemma":[0.9991694,0.0001228808,0.0001368187,0.0003774341,0.00007983435,0.0001135987],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001467085,0.001641625,0.6732872,0.0001188207,0.001344091,0.0000384762,0.00484568,0.005893842,0.1409269,0.000486972,0.00008671985,0.171183],"study_design_scores_gemma":[0.00191164,0.0004410566,0.4741106,0.0003936519,0.0023194,0.0001960107,0.003469686,0.5114964,0.004304212,0.0003768954,0.0004196289,0.0005608501],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.855573,0.0006161703,0.1393579,0.0005140863,0.00006391128,0.0005523897,0.00005706317,0.0002133327,0.003052199],"genre_scores_gemma":[0.9568592,0.0000431634,0.04281654,0.0001259753,0.00001821469,0.00003493547,0.0000640615,0.00002266721,0.00001521955],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5056025,"threshold_uncertainty_score":0.7763014,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01059175587450621,"score_gpt":0.2540954678358054,"score_spread":0.2435037119612992,"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."}}