{"id":"W2609768412","doi":"10.1109/tmi.2017.2697820","title":"Spatially Adaptive Multi-Scale Optimization for Local Parameter Estimation in Cardiac Electrophysiology","year":2017,"lang":"en","type":"article","venue":"IEEE Transactions on Medical Imaging","topic":"Cardiac electrophysiology and arrhythmias","field":"Medicine","cited_by":24,"is_retracted":false,"has_abstract":true,"ca_institutions":"Dalhousie University","funders":"National Heart, Lung, and Blood Institute; National Institutes of Health; National Science Foundation","keywords":"Identifiability; Resolution (logic); Computation; Cardiac electrophysiology; Computer science; Scale (ratio); Image resolution; Estimation theory; Algorithm; Optimization problem; Biological system; Mathematical optimization; Mathematics; Artificial intelligence; Machine learning; Physics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000814551,0.000792961,0.0007710669,0.0004344009,0.0002464553,0.0005429033,0.0005937882,0.0009592538,0.0009432166],"category_scores_gemma":[0.002235828,0.0005247669,0.0007293119,0.0003748633,0.0006698183,0.0006991996,0.0007934448,0.0007936947,0.0002141138],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004151909,"about_ca_system_score_gemma":0.000473055,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002516301,"about_ca_topic_score_gemma":0.002239142,"domain_scores_codex":[0.9997064,0.0001376909,0.00001454799,0.00005988895,0.00006186709,0.00001955444],"domain_scores_gemma":[0.9994037,0.0004235316,0.00005950021,0.00005040397,0.00004418282,0.00001873002],"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":[0.00003076636,0.00001514609,0.0001929993,0.00003991819,0.00001981808,0.00003377696,0.00003004076,0.9741566,0.004648298,0.002552707,0.0002396225,0.01804028],"study_design_scores_gemma":[0.000001992396,0.00000526921,0.00005104842,0.000001747027,0.000001891619,0.000005473216,0.000002357216,0.9985613,0.00037789,0.0008702456,0.0001182091,0.000002593953],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.005897178,0.0001448221,0.9934379,0.00005831103,0.000006911145,0.00001007691,0.00001460533,0.0001289399,0.0003012988],"genre_scores_gemma":[0.4708531,0.0004527959,0.5264676,0.0001275504,0.00006128525,0.0001701623,0.0001321221,0.0002418504,0.001493444],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.002516301,"threshold_uncertainty_score":0.005003393,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01357730924022117,"score_gpt":0.2956097642235233,"score_spread":0.2820324549833021,"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."}}