{"id":"W7118313613","doi":"10.25545/fm1gxy","title":"Transient shear wave elastometry using a portable magnetic resonance sensor","year":2025,"lang":"","type":"dataset","venue":"UNB Dataverse","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of New Brunswick","funders":"","keywords":"Magnetic resonance elastography; Shear waves; Shear (geology); Magnetic resonance imaging; Elasticity (physics); Impulse (physics); Signal processing","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":["metaepi_narrow","research_integrity","insufficient_payload"],"consensus_categories":["metaepi_narrow","research_integrity","insufficient_payload"],"category_scores_codex":[0.002409244,0.003546137,0.003600782,0.005159317,0.001299332,0.000802465,0.003328519,0.002255559,0.03990318],"category_scores_gemma":[0.001628756,0.004344425,0.001033357,0.009270867,0.001646807,0.001633067,0.002508821,0.004305937,0.03776189],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00283645,"about_ca_system_score_gemma":0.004093465,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003159329,"about_ca_topic_score_gemma":0.0004094198,"domain_scores_codex":[0.980866,0.00136192,0.003713505,0.005859458,0.003677727,0.004521443],"domain_scores_gemma":[0.9836659,0.0006642024,0.001526228,0.01180615,0.0008161517,0.001521335],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.001729193,0.001758778,0.00002906279,0.002179986,0.000419831,0.006475842,0.0002122322,0.0007664518,0.001215744,0.00002698225,0.982917,0.002268903],"study_design_scores_gemma":[0.005091244,0.0006739486,0.0001245231,0.004636312,0.004591207,0.0007813094,0.0009732656,0.01244432,0.0002029065,0.000006205905,0.9669204,0.00355436],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.001602517,0.004154594,0.0002338196,0.00002805627,0.003843811,0.003503539,0.9857044,0.0002644947,0.0006647641],"genre_scores_gemma":[0.00008105252,0.004235177,0.01125633,0.0006584568,0.000752501,0.00006800317,0.9759396,0.0004674591,0.006541465],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.01599659,"threshold_uncertainty_score":0.9993545,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02506756645984739,"score_gpt":0.2694568305094777,"score_spread":0.2443892640496303,"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."}}