{"id":"W2154647388","doi":"10.1109/tmi.2003.817767","title":"Three-dimensional edge-preserving image enhancement for computed tomography","year":2003,"lang":"en","type":"article","venue":"IEEE Transactions on Medical Imaging","topic":"Medical Imaging Techniques and Applications","field":"Medicine","cited_by":35,"is_retracted":false,"has_abstract":true,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Image restoration; Deconvolution; Iterative reconstruction; Algorithm; Computer science; Noise reduction; Mathematical optimization; Deblurring; Point spread function; Mathematics; Artificial intelligence; Image processing; Image (mathematics)","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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0006002734,0.0002384185,0.0003355905,0.0002270801,0.0003169827,0.0000349871,0.0002036114,0.0001040768,0.001942485],"category_scores_gemma":[0.0001147841,0.0002094847,0.0002753554,0.0004094666,0.0002844363,0.0001020713,0.000003642246,0.0005819857,0.00004364285],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006935971,"about_ca_system_score_gemma":0.0001997278,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003325739,"about_ca_topic_score_gemma":0.000008136117,"domain_scores_codex":[0.9977314,0.00004109379,0.0004600987,0.000491095,0.0008028671,0.000473419],"domain_scores_gemma":[0.998313,0.0003244761,0.00006961781,0.0004767254,0.0002059874,0.0006101712],"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":[0.0005402541,0.008472237,0.0007501472,0.001113335,0.0007869016,0.0003039312,0.0002631633,0.0001496524,0.1884419,0.005015098,0.34762,0.4465435],"study_design_scores_gemma":[0.006927485,0.000398662,0.0002382673,0.001735413,0.0005462928,0.0004986076,0.00008427724,0.5165848,0.3705152,0.005161733,0.09638704,0.0009222868],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.003629206,0.0001151868,0.9792767,0.01434977,0.0004269238,0.0008562492,0.00001499173,0.0003481583,0.0009828183],"genre_scores_gemma":[0.6664515,0.00003372305,0.3247049,0.007494106,0.000193889,0.0007045869,0.00003322297,0.00007223947,0.0003118966],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.6628222,"threshold_uncertainty_score":0.9989699,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01988746963099422,"score_gpt":0.3101261698338392,"score_spread":0.290238700202845,"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."}}