{"id":"W3013538405","doi":"10.1007/978-3-030-59722-1_5","title":"Patch-Based Non-local Bayesian Networks for Blind Confocal Microscopy Denoising","year":2020,"lang":"en","type":"preprint","venue":"Lecture notes in computer science","topic":"Image and Signal Denoising Methods","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Artificial intelligence; Noise reduction; Leverage (statistics); Computer science; Pattern recognition (psychology); Posterior probability; Maximum a posteriori estimation; Convolutional neural network; Noise (video); Bayesian probability; Gaussian noise; Shot noise; Gaussian; Machine learning; Computer vision; Mathematics; Statistics; Physics; 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":["metaepi_narrow","scholarly_communication","open_science"],"consensus_categories":[],"category_scores_codex":[0.002380491,0.0007453376,0.0009218949,0.0005993987,0.0005134508,0.002315419,0.005590325,0.0005868108,0.000003363575],"category_scores_gemma":[0.000310321,0.0007197171,0.0003312908,0.001988005,0.0009478689,0.000464133,0.002895618,0.001693945,0.00000631018],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003436918,"about_ca_system_score_gemma":0.001865954,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001466116,"about_ca_topic_score_gemma":0.00004625011,"domain_scores_codex":[0.9940114,0.0002882943,0.0008177072,0.0026709,0.0008856172,0.001326081],"domain_scores_gemma":[0.995657,0.00144229,0.0003770764,0.001694914,0.0004212639,0.0004074507],"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":[0.00007477405,0.0000520259,0.00009594542,0.0001037189,0.00001005998,0.00008089559,0.0005511353,0.6331722,0.002162791,0.00007463939,0.00003878405,0.363583],"study_design_scores_gemma":[0.001348543,0.0002504319,0.0001524281,0.0004148083,0.00001607743,0.00001931247,4.728855e-7,0.9298359,0.0460091,0.02114277,0.00005590076,0.0007542772],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.001044781,0.0003153154,0.9896606,0.002923236,0.004729646,0.001069654,0.000008087599,0.0002390531,0.000009581649],"genre_scores_gemma":[0.4599995,0.000002890275,0.535484,0.003899041,0.0005460422,0.00003252712,0.000009282892,0.00002627643,4.918241e-7],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.4589547,"threshold_uncertainty_score":0.9997899,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02560165186904007,"score_gpt":0.3146057455498374,"score_spread":0.2890040936807973,"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."}}