{"id":"W1965774271","doi":"10.1117/12.765943","title":"Speckle reduction method for optical coherence tomography using interval type II fuzzy set","year":2008,"lang":"en","type":"article","venue":"Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE","topic":"Optical Coherence Tomography Applications","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada; University of Waterloo","keywords":"Speckle noise; Speckle pattern; Optical coherence tomography; Artificial intelligence; Thresholding; Wavelet; Noise reduction; Wavelet transform; Mathematics; Computer vision; Noise (video); Fuzzy set; Reduction (mathematics); Computer science; Pattern recognition (psychology); Fuzzy logic; Algorithm; Image (mathematics); Optics; Physics","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0005707746,0.0004488133,0.0005643041,0.0002167009,0.000226614,0.00008458517,0.0009746857,0.0003070255,0.00002153613],"category_scores_gemma":[0.0003332426,0.0004225302,0.0008838091,0.0009560664,0.000400458,0.0006102506,0.0001653118,0.0004680451,0.000002070706],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001894411,"about_ca_system_score_gemma":0.00004390738,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000009302401,"about_ca_topic_score_gemma":1.760875e-7,"domain_scores_codex":[0.9973775,3.125718e-8,0.0008771303,0.0005159301,0.0006340572,0.000595405],"domain_scores_gemma":[0.997376,0.000187879,0.000240796,0.0001158055,0.001866314,0.0002132524],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0001173472,0.0001509755,0.0001767922,0.0005014405,0.0006008681,1.362083e-7,0.0003208697,0.00188614,0.7251676,0.2668986,0.003710681,0.0004685589],"study_design_scores_gemma":[0.002436674,0.001426037,0.001501965,0.0006513368,0.0007670678,0.0002840972,0.002322356,0.3863268,0.5869836,0.009573389,0.006108813,0.001617886],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.9926977,0.0001375829,0.002180204,0.0005306534,0.0004496384,0.001095787,0.00009768153,0.0002931425,0.002517671],"genre_scores_gemma":[0.4453354,0.00006460576,0.5536102,0.00002789325,0.0004804197,0.0002949521,0.0000179917,0.00009663424,0.0000718619],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.55143,"threshold_uncertainty_score":0.9998227,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02610928613234123,"score_gpt":0.2685255137074354,"score_spread":0.2424162275750942,"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."}}