{"id":"W2102618784","doi":"10.1109/iembs.1995.575241","title":"Sharpening enhancement of digitized mammograms with complex symmetric Daubechies wavelets","year":2002,"lang":"en","type":"article","venue":"","topic":"Image and Signal Denoising Methods","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal","funders":"","keywords":"Sharpening; Wavelet; Computer vision; Artificial intelligence; Computer science; Daubechies wavelet; Wavelet transform; Pattern recognition (psychology); Discrete wavelet transform","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":[0.0002825118,0.0001432156,0.0002546367,0.0002238348,0.00008750697,0.0001686253,0.0005947048,0.00002861051,0.0002998495],"category_scores_gemma":[0.00003677103,0.0001035067,0.00005843605,0.00102153,0.00006401532,0.0003670219,0.0001622832,0.00008123377,0.00004412796],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001809028,"about_ca_system_score_gemma":0.00001081538,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003700426,"about_ca_topic_score_gemma":0.000002175012,"domain_scores_codex":[0.9986694,0.00007837795,0.000265377,0.0003008399,0.000397841,0.0002881637],"domain_scores_gemma":[0.9991116,0.0001733379,0.0001047189,0.0004340152,0.0001059225,0.00007043459],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00004763985,0.0004298687,0.0004661591,0.00008685003,0.0001189678,0.0001075282,0.001267298,0.00003390343,0.05074063,0.06594279,0.00432762,0.8764307],"study_design_scores_gemma":[0.006804805,0.002194293,0.004249463,0.0002780256,0.00006644266,0.0002235457,0.0001356512,0.3614792,0.5967104,0.006836514,0.01951939,0.001502207],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01648451,0.0001301701,0.927486,0.0002484137,0.00005970423,0.0001272943,6.547114e-7,0.0001281686,0.05533507],"genre_scores_gemma":[0.5799901,0.00001255468,0.417428,0.0002175982,0.00001307162,0.000004972949,0.0000012494,0.000006118135,0.002326305],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8749285,"threshold_uncertainty_score":0.4220881,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04977230607607368,"score_gpt":0.2635393971199853,"score_spread":0.2137670910439116,"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."}}