{"id":"W2117143659","doi":"10.1109/tbme.2007.894956","title":"Time-Varying Causal Coherence Function and Its Application to Renal Blood Pressure and Blood Flow Data","year":2007,"lang":"en","type":"article","venue":"IEEE Transactions on Biomedical Engineering","topic":"Cardiac electrophysiology and arrhythmias","field":"Medicine","cited_by":21,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Victoria","funders":"National Heart, Lung, and Blood Institute","keywords":"Coherence (philosophical gambling strategy); Feed forward; Causal model; Computer science; Function (biology); Blood flow; Renal blood flow; Algorithm; Control theory (sociology); Hemodynamics; Mathematics; Artificial intelligence; Control engineering; Cardiology; Medicine; Statistics; Control (management); Engineering","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":[0.0002034512,0.0001421662,0.0001983555,0.0001599463,0.00009069843,0.00000837983,0.00005520809,0.000156835,0.00002854982],"category_scores_gemma":[0.0000205626,0.0001351856,0.00002713136,0.0002583202,0.00004637456,0.0000840131,0.000005062333,0.0003341148,0.00001472548],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000007155582,"about_ca_system_score_gemma":0.00002518298,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000005710937,"about_ca_topic_score_gemma":0.000001415698,"domain_scores_codex":[0.9990167,0.000009966071,0.0001786038,0.0003722829,0.0001789832,0.000243436],"domain_scores_gemma":[0.9993224,0.0001012279,0.00001990464,0.0002497445,0.00002771821,0.0002790389],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002623102,0.0001581955,0.000002958106,0.0001005159,0.0004218164,0.00005173965,0.00005278713,0.00606114,0.9631426,0.00001144215,0.00004430419,0.02969016],"study_design_scores_gemma":[0.007233896,0.003381984,0.0046543,0.0006391082,0.006510464,0.002887855,0.00002555155,0.6796533,0.2849763,0.00002470329,0.008995159,0.001017325],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.276368,0.0007687705,0.7215726,0.0003624665,0.0002246626,0.0004280589,0.00006818181,0.0001803014,0.00002696175],"genre_scores_gemma":[0.9978015,0.00006985822,0.001552804,0.0001210705,0.0002634561,0.00002442926,0.00004541369,0.00001912727,0.000102273],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7214335,"threshold_uncertainty_score":0.5512711,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008373432229944121,"score_gpt":0.2327809738815008,"score_spread":0.2244075416515567,"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."}}