{"id":"W2096235391","doi":"10.1109/icip.1994.413891","title":"A moving target evaluating algorithms for removing MRI motion artifacts","year":2002,"lang":"en","type":"article","venue":"","topic":"Medical Imaging Techniques and Applications","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Computer science; Artificial intelligence; Image processing; Motion (physics); Computer vision; Algorithm; Field (mathematics); Autoregressive model; Signal processing; Image (mathematics); Mathematics; Digital 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002797533,0.001413094,0.001158847,0.002166899,0.0007205949,0.00187047,0.001658711,0.00178796,0.006823561],"category_scores_gemma":[0.005969189,0.0005328328,0.001220023,0.001748189,0.0006005621,0.002057194,0.001055085,0.001745138,0.0038215],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006220817,"about_ca_system_score_gemma":0.0009585923,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001965177,"about_ca_topic_score_gemma":0.00236025,"domain_scores_codex":[0.998439,0.000328553,0.0001175787,0.000253545,0.0007857023,0.00007560439],"domain_scores_gemma":[0.9973269,0.000843606,0.0001771735,0.0003111159,0.001261973,0.00007924091],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002501911,0.00008335216,0.0004187862,0.0001569461,0.000103807,0.00008309563,0.0000717475,0.03403567,0.02889188,0.02375498,0.005717213,0.9064323],"study_design_scores_gemma":[0.00007940529,0.0004439612,0.001394466,0.0000476466,0.0001315743,0.0004733769,0.00004314244,0.912777,0.04276454,0.0182962,0.0234652,0.00008351281],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.001291073,0.0002893544,0.9968536,0.00005783436,0.00005782918,0.00004276322,0.00002377936,0.0005298901,0.0008538],"genre_scores_gemma":[0.01580984,0.0003506302,0.978934,0.0000793074,0.00008032472,0.00009738803,0.0001578653,0.0002353706,0.004255167],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006823561,"threshold_uncertainty_score":0.02282709,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1018470843665821,"score_gpt":0.3806409480355696,"score_spread":0.2787938636689875,"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."}}