{"id":"W4413837861","doi":"10.1002/mrm.70058","title":"Predictive signal modeling and multi‐rate filtering in accelerated cardiac <scp>MRI</scp>","year":2025,"lang":"en","type":"article","venue":"Magnetic Resonance in Medicine","topic":"Advanced MRI Techniques and Applications","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto; Sunnybrook Health Science Centre; Health Sciences Centre; Ted Rogers Centre for Heart Research","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research; Canada Foundation for Innovation","keywords":"SIGNAL (programming language); Computer science; Chemistry","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001284952,0.0006268892,0.0005005815,0.0003803988,0.0002704312,0.0007411026,0.0008848087,0.0008584653,0.00103884],"category_scores_gemma":[0.002749454,0.0003945006,0.0007157717,0.0003469404,0.0005924329,0.0007916673,0.0006485478,0.001063473,0.0003343608],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006544502,"about_ca_system_score_gemma":0.001086252,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009226755,"about_ca_topic_score_gemma":0.006480166,"domain_scores_codex":[0.9996443,0.00009315468,0.00001810131,0.00006512318,0.0001498748,0.00002941814],"domain_scores_gemma":[0.9991289,0.0004522643,0.0001378061,0.00008129167,0.0001617241,0.00003805169],"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":[0.00008810632,0.00003509805,0.0008054788,0.00007503338,0.00003491097,0.0001037254,0.00006955356,0.9122586,0.009052377,0.008976425,0.0008467392,0.06765393],"study_design_scores_gemma":[0.000001734672,0.0000123814,0.00007841475,0.000003450222,0.000002950974,0.00001159986,0.000002086293,0.9979882,0.0009633688,0.0006191822,0.0003129691,0.000003693283],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.009939236,0.0001429667,0.9888108,0.0001657403,0.00002100798,0.00001831891,0.00003011678,0.0003427282,0.0005291137],"genre_scores_gemma":[0.5434417,0.00070959,0.4524661,0.0001975593,0.00008951363,0.0001511842,0.0003139638,0.0002079463,0.002422521],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.009226755,"threshold_uncertainty_score":0.01834613,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02879726495569611,"score_gpt":0.323998940557834,"score_spread":0.2952016756021379,"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."}}