{"id":"W2007755353","doi":"10.1007/s11517-008-0371-6","title":"EIT image reconstruction with four dimensional regularization","year":2008,"lang":"en","type":"article","venue":"Medical & Biological Engineering & Computing","topic":"Electrical and Bioimpedance Tomography","field":"Engineering","cited_by":15,"is_retracted":false,"has_abstract":false,"ca_institutions":"Carleton University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Electrical impedance tomography; Iterative reconstruction; Regularization (linguistics); A priori and a posteriori; Image resolution; Artificial intelligence; Temporal resolution; Computer vision; Computer science; Reconstruction algorithm; Tomography; Algorithm; Mathematics; Physics; Optics","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":[0.0005202425,0.0006289677,0.0005217798,0.0005940862,0.0002409838,0.001221094,0.0007911439,0.001257629,0.005063291],"category_scores_gemma":[0.001530123,0.0004636834,0.0006530107,0.0008120455,0.0004019162,0.0007103867,0.0009095363,0.001431503,0.001829916],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002373586,"about_ca_system_score_gemma":0.0007837849,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0012236,"about_ca_topic_score_gemma":0.001174878,"domain_scores_codex":[0.9998226,0.00004303349,0.00001398719,0.00002253236,0.00007994323,0.00001801792],"domain_scores_gemma":[0.9996622,0.0000854779,0.00004258315,0.0001075356,0.00007325941,0.00002901026],"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":[0.0007696337,0.0002029659,0.002415235,0.0004585664,0.0002015447,0.001003905,0.0002774,0.4084389,0.2112946,0.07843351,0.01071194,0.2857918],"study_design_scores_gemma":[0.00003871152,0.00003901167,0.0006609144,0.00002518593,0.00003286582,0.0007238777,0.00003580776,0.9307851,0.04971812,0.009026822,0.008874524,0.00003905914],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01203975,0.0001376404,0.9830352,0.0002562969,0.00004811718,0.00002949473,0.0001782659,0.0007344391,0.003540779],"genre_scores_gemma":[0.1199151,0.0002811705,0.8691645,0.0002168908,0.00003784776,0.00007577574,0.0005951871,0.0005225809,0.009190992],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005063291,"threshold_uncertainty_score":0.01693839,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009098572449885278,"score_gpt":0.1753474070784868,"score_spread":0.1662488346286015,"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."}}