{"meta":{"page":1,"per_page":50,"max_per_page":100,"total":1335,"total_is_capped":false,"direct_labels_cover":0,"predictions_cover":1335,"direct_label_status":"direct model label, unvalidated","prediction_status":"machine_predicted_unvalidated (Codex and Gemma teacher distillation)","score_status":"score_only:v0-immature-baseline (scores rank; they never assert a category)","snapshot":{"source":"OpenAlex, pinned release, all 482 partitions","release":"2026-06-24","frame_built":"2026-07-12","author_layer_release":"2026-06-26"},"query_hash":"b0a089017f0d","filters":{"topic":"Image and Signal Denoising Methods"}},"results":[{"id":"W1899329334","doi":"10.1002/jmri.22003","title":"Adaptive non‐local means denoising of MR images with spatially varying noise levels","year":2009,"lang":"en","type":"article","venue":"Journal of Magnetic Resonance Imaging","topic":"Image and Signal Denoising Methods","field":"Computer Science","cited_by":1133,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"McGill University; Montreal Neurological Institute and Hospital","funders":"","keywords":"Noise (video); Computer science; Noise reduction; Filter (signal processing); Gaussian noise; Artificial intelligence; Bilateral filter; Rician fading; Median filter; Computer vision; Image noise; Sensitivity (control systems); Adaptive filter; Pattern recognition (psychology); Image processing; Image (mathematics); Algorithm","authors":[{"name":"José V. Manjón","is_ca":false},{"name":"Pierrick Coupé","is_ca":true},{"name":"Luis Martí‐Bonmatí","is_ca":false},{"name":"D. Louis Collins","is_ca":true},{"name":"Montserrat Robles","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.01618067395139978,"gpt":0.2612123504434333,"spread":0.2450316764920335,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001039634,0.0005854096,0.000585675,0.0005570767,0.0002228162,0.0003624302,0.0006041756,0.0009559336,0.0005433034],"category_scores_gemma":[0.002633988,0.0002540709,0.0006448446,0.0004012698,0.0005561631,0.0005895169,0.0004200593,0.0005594096,0.0002472578],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003876387,"about_ca_system_score_gemma":0.0004728096,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00135365,"about_ca_topic_score_gemma":0.002754785,"domain_scores_codex":[0.9995883,0.0001126857,0.00002308024,0.00008948516,0.0001606522,0.00002573273],"domain_scores_gemma":[0.9993078,0.0002996501,0.0001096579,0.00009269032,0.0001717069,0.0000185494],"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":[0.0006217566,0.00017525,0.002983724,0.0004819705,0.0002624267,0.0003267617,0.000325229,0.2084091,0.3112925,0.006895378,0.001761393,0.4664646],"study_design_scores_gemma":[0.00003022618,0.000156633,0.002682046,0.00001887878,0.00007216185,0.0003363939,0.00002808864,0.8983087,0.09277099,0.00324598,0.002315978,0.00003390266],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.05472679,0.0002254415,0.9441589,0.00009494662,0.00003585557,0.00002374927,0.00001790505,0.0002317056,0.000484787],"genre_scores_gemma":[0.390352,0.0003545198,0.6057253,0.0001081548,0.00007760609,0.00009413705,0.0001269134,0.00009849289,0.003063],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.00135365,"threshold_uncertainty_score":0.00549823,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W1654995442","doi":"10.1007/978-1-4612-1258-4","title":"Coherent States, Wavelets and Their Generalizations","year":2000,"lang":"en","type":"book","venue":"Graduate texts in contemporary physics","topic":"Image and Signal Denoising Methods","field":"Computer Science","cited_by":737,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Concordia University","funders":"","keywords":"Wavelet; Psychology; Psychoanalysis; Epistemology; Philosophy; Computer science; Artificial intelligence","authors":[{"name":"S. Twareque Ali","is_ca":true},{"name":"Jean-Pierre Antoine","is_ca":false},{"name":"Jean‐Pierre Gazeau","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.0976335104763195,"gpt":0.2895478813492841,"spread":0.1919143708729645,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000585322,0.0008234688,0.0007521419,0.001777637,0.0007641321,0.002997931,0.000606269,0.001435133,0.00392666],"category_scores_gemma":[0.001837005,0.0005447125,0.0004238421,0.002906865,0.002862942,0.00398912,0.0007713954,0.003415206,0.001258031],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006310212,"about_ca_system_score_gemma":0.0005098187,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007189959,"about_ca_topic_score_gemma":0.0007480889,"domain_scores_codex":[0.9997835,0.00004473222,0.00001666255,0.00005578894,0.00008363721,0.00001564522],"domain_scores_gemma":[0.9996591,0.0001968774,0.00003399469,0.00004008809,0.00005063016,0.00001938515],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.000005163811,0.000008153442,0.00003403523,0.00007383101,0.000004961322,0.00001974126,0.0001379188,0.0006695546,0.0004205418,0.9705395,0.008679179,0.01940738],"study_design_scores_gemma":[0.000003010399,0.000007383625,0.00009587329,0.00002753976,0.000003769281,0.00005905726,0.00003994509,0.001480039,0.00008619125,0.9700325,0.02815904,0.000005645296],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"other","genre_gemma":"other","genre_scores_codex":[0.02123794,0.3239284,0.280865,0.008500029,0.006384967,0.00005123429,0.0003754135,0.0003379136,0.358319],"genre_scores_gemma":[0.3120259,0.2456921,0.1351325,0.004067297,0.01770956,0.000382334,0.0007214309,0.0003919334,0.283877],"genre_candidate":"other","genre_consensus":"other","teacher_disagreement_score":0.00392666,"threshold_uncertainty_score":0.01313603,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W1995194116","doi":"10.1561/0600000020","title":"Bilateral Filtering: Theory and Applications","year":2009,"lang":"en","type":"article","venue":"Foundations and Trends® in Computer Graphics and Vision","topic":"Image and Signal Denoising Methods","field":"Computer Science","cited_by":572,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Computer science","authors":[{"name":"Sylvain Paris","is_ca":false},{"name":"Pierre Kornprobst","is_ca":false},{"name":"Jack Tumblin","is_ca":false},{"name":"Frédo Durand","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.01908476934478026,"gpt":0.3295236601896532,"spread":0.3104388908448729,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009431016,0.0008535994,0.001188158,0.001743878,0.0006219664,0.002462851,0.001085572,0.002229502,0.00833486],"category_scores_gemma":[0.003355159,0.000504037,0.0008772066,0.003175804,0.001418509,0.002374179,0.001355135,0.002134363,0.003698215],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009497119,"about_ca_system_score_gemma":0.0008373887,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001792205,"about_ca_topic_score_gemma":0.0007797445,"domain_scores_codex":[0.9989972,0.0001599169,0.00006632477,0.0001873269,0.0005262439,0.00006306305],"domain_scores_gemma":[0.999176,0.0003542419,0.00006683372,0.000093453,0.0002714019,0.00003798148],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.000044721,0.00004711606,0.0004707019,0.000714645,0.00007540838,0.0002067973,0.0001893525,0.04822148,0.005235611,0.5255125,0.02749408,0.3917875],"study_design_scores_gemma":[0.00001876617,0.00006158223,0.0004984829,0.0002070718,0.00004220227,0.0007034652,0.0000666468,0.2374419,0.002157161,0.6379633,0.1207803,0.00005920915],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.001328798,0.02159621,0.9559066,0.0009270027,0.0004063276,0.00003610415,0.0001370806,0.0003566819,0.01930517],"genre_scores_gemma":[0.1917452,0.09462888,0.6694002,0.001721982,0.004409111,0.0005058582,0.0008129186,0.0004093713,0.03636651],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.00833486,"threshold_uncertainty_score":0.02788281,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2020219450","doi":"10.1046/j.1365-246x.2003.01766.x","title":"Fast inversion of large-scale magnetic data using wavelet transforms and a logarithmic barrier method","year":2003,"lang":"en","type":"article","venue":"Geophysical Journal International","topic":"Image and Signal Denoising Methods","field":"Computer Science","cited_by":425,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of British Columbia","funders":"","keywords":"Conjugate gradient method; Wavelet; Wavelet transform; Algorithm; Mathematics; Logarithm; Coefficient matrix; Matrix (chemical analysis); Solver; Stationary wavelet transform; Wavelet packet decomposition; Computer science; Mathematical optimization; Mathematical analysis; Artificial intelligence; Eigenvalues and eigenvectors; Physics","authors":[{"name":"Yaoguo Li","is_ca":true},{"name":"Douglas W. Oldenburg","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.02818622910144782,"gpt":0.3187276320347983,"spread":0.2905414029333505,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001101915,0.0005847666,0.0005697466,0.0005577073,0.0002097637,0.0005701972,0.000559477,0.0005141081,0.000967385],"category_scores_gemma":[0.002495431,0.0003226173,0.0005186339,0.0006290169,0.0005801891,0.001212386,0.0007460944,0.0009514654,0.0004833906],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001966613,"about_ca_system_score_gemma":0.0005419111,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007711333,"about_ca_topic_score_gemma":0.0008304363,"domain_scores_codex":[0.9997649,0.0000590169,0.00001332345,0.00002750927,0.0001188398,0.00001639271],"domain_scores_gemma":[0.9994199,0.0003631814,0.00005667784,0.00005809683,0.0000817918,0.00002021323],"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":[0.0003349486,0.00009208553,0.0009622631,0.0003956203,0.00008944511,0.000546146,0.0003212146,0.4508784,0.1265466,0.1061239,0.002225483,0.3114839],"study_design_scores_gemma":[0.00001691374,0.00002901584,0.0001703233,0.00000763159,0.000006003418,0.0000930298,0.00002068247,0.9727302,0.01248024,0.01257322,0.001860227,0.00001256984],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.003956056,0.00005030902,0.9955233,0.0000518816,0.00001193748,0.00001037736,0.00001139347,0.00009258408,0.0002921045],"genre_scores_gemma":[0.1068786,0.0004155952,0.8905526,0.00005780893,0.00003613611,0.00009090007,0.0001477025,0.0001995277,0.00162119],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.001101915,"threshold_uncertainty_score":0.005827546,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2098976973","doi":"10.1111/j.1467-6419.2006.00502.x","title":"A GUIDE TO WAVELETS FOR ECONOMISTS*","year":2007,"lang":"en","type":"article","venue":"Journal of Economic Surveys","topic":"Image and Signal Denoising Methods","field":"Computer Science","cited_by":410,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true},"ca_institutions":"","funders":"","keywords":"Wavelet; Exploratory analysis; Economics; Economic analysis; Engineering economics; Computer science; Data science; Econometrics; Management science; Industrial engineering; Engineering; Artificial intelligence; Classical economics; Finance","authors":[{"name":"Patrick M. Crowley","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.03588054295367798,"gpt":0.339586169396499,"spread":0.3037056264428211,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00254715,0.001374187,0.001034584,0.002721139,0.0006962814,0.00224206,0.001331497,0.002128533,0.02421809],"category_scores_gemma":[0.009446422,0.0006909377,0.0006030409,0.003327377,0.001766733,0.003853346,0.001206979,0.006424665,0.02496192],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009770413,"about_ca_system_score_gemma":0.001396686,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001575206,"about_ca_topic_score_gemma":0.001887847,"domain_scores_codex":[0.9990631,0.0003391582,0.0001166702,0.0001154578,0.000304528,0.00006112504],"domain_scores_gemma":[0.9954805,0.002911424,0.0001778797,0.0003352471,0.0009422731,0.0001526881],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00001673094,0.00003757382,0.0001596073,0.0003182707,0.00001262472,0.0001115351,0.0002532281,0.0008840579,0.0003261123,0.3424045,0.553688,0.1017876],"study_design_scores_gemma":[0.000008917891,0.00001160867,0.0001618628,0.0002886071,0.000002619826,0.0001097905,0.00006992798,0.001016398,0.0000790093,0.1904583,0.8077806,0.00001236882],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.001452306,0.191758,0.4149641,0.1239955,0.02353357,0.0003419187,0.004864657,0.003427486,0.2356625],"genre_scores_gemma":[0.02767563,0.2649712,0.4337568,0.0281469,0.02120224,0.00160565,0.003846707,0.002718246,0.2160767],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.02421809,"threshold_uncertainty_score":0.08101755,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2161073299","doi":"10.1109/tgrs.2010.2075937","title":"Denoising of Hyperspectral Imagery Using Principal Component Analysis and Wavelet Shrinkage","year":2010,"lang":"en","type":"article","venue":"IEEE Transactions on Geoscience and Remote Sensing","topic":"Image and Signal Denoising Methods","field":"Computer Science","cited_by":404,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Canadian Space Agency","funders":"","keywords":"Hyperspectral imaging; Principal component analysis; Wavelet; Shrinkage; Artificial intelligence; Noise reduction; Wavelet transform; Pattern recognition (psychology); Remote sensing; Computer science; Geology; Computer vision","authors":[{"name":"Guangyi Chen","is_ca":true},{"name":"Shen‐En Qian","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.01854443229063702,"gpt":0.2706779494589611,"spread":0.2521335171683241,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009105631,0.000970259,0.001182657,0.001129498,0.000385462,0.0006735335,0.0007883824,0.0008144439,0.0004722945],"category_scores_gemma":[0.00185626,0.000455302,0.001258166,0.001264641,0.0006748404,0.001364766,0.0008203084,0.001178006,0.0003895985],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002921514,"about_ca_system_score_gemma":0.000449438,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009261223,"about_ca_topic_score_gemma":0.001272805,"domain_scores_codex":[0.9992798,0.0001126273,0.00004072572,0.0001411384,0.0003929078,0.00003285769],"domain_scores_gemma":[0.9993506,0.000205479,0.00008206035,0.00009122353,0.0002492228,0.00002140464],"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":[0.0002178032,0.0001422874,0.001733993,0.000479226,0.0002261972,0.0003423482,0.000277384,0.1401615,0.2583949,0.01460389,0.002977534,0.580443],"study_design_scores_gemma":[0.00001091966,0.00006110736,0.001204672,0.00001814114,0.00005483493,0.0002958372,0.00002830742,0.9313089,0.05862439,0.003665496,0.004685586,0.00004196528],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.00743924,0.0002745519,0.9915828,0.00006847589,0.00004275751,0.00001830771,0.00001940087,0.0001862675,0.0003681639],"genre_scores_gemma":[0.08188199,0.001299689,0.9147551,0.00008397241,0.0001061037,0.00008814276,0.00023315,0.0001224277,0.001429414],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.001182657,"threshold_uncertainty_score":0.004815578,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2152178471","doi":"10.1109/tip.2005.857260","title":"Color demosaicking via directional linear minimum mean square-error estimation","year":2005,"lang":"en","type":"article","venue":"IEEE Transactions on Image Processing","topic":"Image and Signal Denoising Methods","field":"Computer Science","cited_by":381,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"McMaster University","funders":"","keywords":"Demosaicing; Color filter array; Mean squared error; Mathematics; Artificial intelligence; Color difference; Bayer filter; Minimum mean square error; Computer vision; Primary color; Color image; Color gel; Filter (signal processing); Computer science; Image (mathematics); Image processing; Statistics","authors":[{"name":"Lei Zhang","is_ca":true},{"name":"Xiaolin Wu","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.02469161643321526,"gpt":0.3084163698651311,"spread":0.2837247534319158,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005962379,0.0006554052,0.0005270447,0.0006473066,0.0002004312,0.0004441875,0.0005276909,0.0004407888,0.0009113143],"category_scores_gemma":[0.001653841,0.0003551192,0.000550656,0.0005767153,0.0003192036,0.0005258595,0.0005566836,0.000496897,0.0006812971],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003050574,"about_ca_system_score_gemma":0.0004775114,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001650796,"about_ca_topic_score_gemma":0.002933014,"domain_scores_codex":[0.9995891,0.00009992754,0.00002452286,0.0000735231,0.0001833727,0.00002951017],"domain_scores_gemma":[0.999615,0.00009165776,0.00005292772,0.00007890489,0.0001450875,0.00001635428],"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":[0.0003436488,0.00007912656,0.001798479,0.0001928867,0.0001102265,0.000122843,0.0001303597,0.1488454,0.2436348,0.008163166,0.002559332,0.5940198],"study_design_scores_gemma":[0.0000301878,0.00007119316,0.001057072,0.00001317245,0.00003082031,0.0002896527,0.00003189976,0.8894184,0.1028227,0.00277216,0.003428436,0.00003432107],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.005702748,0.00005004549,0.993524,0.0000300596,0.00001386167,0.00001101063,0.00001877891,0.0002926265,0.000356907],"genre_scores_gemma":[0.07873016,0.0001449264,0.9199129,0.00003676355,0.00001590934,0.00002524971,0.00009354496,0.00004663407,0.0009939094],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.001650796,"threshold_uncertainty_score":0.003282428,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2106783469","doi":"10.1109/tgrs.2002.803727","title":"A review of speckle filtering in the context of estimation theory","year":2002,"lang":"en","type":"review","venue":"IEEE Transactions on Geoscience and Remote Sensing","topic":"Image and Signal Denoising Methods","field":"Computer Science","cited_by":376,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Natural Resources Canada","funders":"","keywords":"Speckle pattern; Speckle noise; Multiplicative noise; Filter (signal processing); Context (archaeology); Computer science; Multiplicative function; Artificial intelligence; Computer vision; Algorithm; Mathematics; Telecommunications","authors":[{"name":"R. Touzi","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.05427027881734388,"gpt":0.3268050684782492,"spread":0.2725347896609054,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001105474,0.001080784,0.001795565,0.00265731,0.0005221136,0.001586948,0.001567804,0.00202578,0.003402147],"category_scores_gemma":[0.002366705,0.0005970485,0.0006178678,0.005343335,0.00143322,0.003009936,0.0006933969,0.001835054,0.003101433],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009777127,"about_ca_system_score_gemma":0.001142152,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001972844,"about_ca_topic_score_gemma":0.001468334,"domain_scores_codex":[0.9992311,0.0002042605,0.00009159031,0.0001378803,0.0002970904,0.0000381194],"domain_scores_gemma":[0.9984971,0.000970273,0.00007109417,0.00006896883,0.0003618743,0.00003072906],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0000384413,0.00007690242,0.0004060103,0.00756425,0.00009377657,0.0003725709,0.0002006235,0.004568723,0.001727164,0.09523802,0.03961843,0.850095],"study_design_scores_gemma":[0.000009578555,0.0001009059,0.001268061,0.002761263,0.00006940741,0.001865546,0.00009462941,0.004436549,0.0009804242,0.06083678,0.9275171,0.00005981284],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.0003726481,0.9617665,0.02790604,0.0008239524,0.001088118,0.00001588463,0.00003119514,0.00004649627,0.007949148],"genre_scores_gemma":[0.00545623,0.9731562,0.01482621,0.0006445514,0.002398219,0.00003735759,0.00007474143,0.00002524058,0.003381259],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.003402147,"threshold_uncertainty_score":0.01138133,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2021586009","doi":"10.1109/tip.2007.902329","title":"Median Filtering in Constant Time","year":2007,"lang":"en","type":"letter","venue":"IEEE Transactions on Image Processing","topic":"Image and Signal Denoising Methods","field":"Computer Science","cited_by":313,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Université Laval","funders":"","keywords":"Kernel (algebra); Constant (computer programming); Algorithm; Computational complexity theory; Filter (signal processing); Simple (philosophy); Image processing; Computer science; Mathematics; Image (mathematics); Computer vision; Artificial intelligence; Discrete mathematics","authors":[{"name":"Simon Perreault","is_ca":true},{"name":"P. Hébert","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.02287642240894619,"gpt":0.2833681163560103,"spread":0.2604916939470641,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005345239,0.0007948993,0.0006589373,0.000663834,0.0004979057,0.001664994,0.0006801747,0.001382886,0.009987389],"category_scores_gemma":[0.00354304,0.0002932562,0.0003439065,0.001053521,0.0004892223,0.001775114,0.000572635,0.001085222,0.005897626],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00103566,"about_ca_system_score_gemma":0.0006446812,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001057773,"about_ca_topic_score_gemma":0.00167189,"domain_scores_codex":[0.9989113,0.0001091162,0.00004872753,0.0001993638,0.0006426342,0.00008885516],"domain_scores_gemma":[0.9990678,0.0003996604,0.00004608318,0.0002613473,0.0001989813,0.00002608954],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0004967607,0.00006568024,0.0003789881,0.0002991867,0.00007403258,0.0003667058,0.00009404654,0.05145139,0.04465258,0.1405777,0.07052641,0.6910166],"study_design_scores_gemma":[0.00009414876,0.00008300703,0.0003726441,0.00005884693,0.00002735235,0.0007719546,0.00004428553,0.7344835,0.02909554,0.1068667,0.1280622,0.0000398209],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.003460043,0.0008165837,0.9770336,0.001252528,0.0005326529,0.00003949153,0.0001187399,0.002589509,0.01415693],"genre_scores_gemma":[0.1502926,0.002140848,0.8130965,0.001039872,0.0008726732,0.0002663718,0.0006475127,0.0004436928,0.03119994],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.009987389,"threshold_uncertainty_score":0.03341115,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2108207814","doi":"10.1109/tmi.2003.816958","title":"Noise reduction for magnetic resonance images via adaptive multiscale products thresholding","year":2003,"lang":"en","type":"article","venue":"IEEE Transactions on Medical Imaging","topic":"Image and Signal Denoising Methods","field":"Computer Science","cited_by":294,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"McMaster University","funders":"","keywords":"Wavelet; Thresholding; Artificial intelligence; Noise reduction; Noise (video); Wavelet transform; Pattern recognition (psychology); Computer vision; Computer science; Stationary wavelet transform; Second-generation wavelet transform; Canny edge detector; Edge detection; Lifting scheme; Wavelet packet decomposition; Mathematics; Image processing; Image (mathematics)","authors":[{"name":"Paul Bao","is_ca":true},{"name":"Lei Zhang","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.02070692670888719,"gpt":0.2815719184529481,"spread":0.2608649917440609,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005933622,0.0005256019,0.0005081691,0.0005675585,0.0001906055,0.0004184537,0.0004334918,0.0005794808,0.0007743787],"category_scores_gemma":[0.001611067,0.000268081,0.0005480826,0.0006005092,0.0004765576,0.0007129452,0.0005027408,0.0005380128,0.0004428502],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001734867,"about_ca_system_score_gemma":0.0002456757,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004337156,"about_ca_topic_score_gemma":0.0007416306,"domain_scores_codex":[0.9996939,0.00005263466,0.00001539673,0.00004719067,0.0001771635,0.00001373637],"domain_scores_gemma":[0.9997022,0.0001219192,0.00004506169,0.00004226583,0.00007600575,0.00001252938],"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":[0.0003112586,0.00006040126,0.0007374671,0.0002753863,0.00008277239,0.0002576159,0.0001250145,0.04616607,0.466853,0.009140736,0.001461769,0.4745284],"study_design_scores_gemma":[0.0000361536,0.0002982851,0.00207872,0.00002632807,0.0001027434,0.00084893,0.00003423855,0.7731024,0.2068577,0.008907505,0.007675719,0.00003122272],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.03669235,0.0008997872,0.9609116,0.0001215693,0.00004264401,0.00002169543,0.00002377958,0.0003388213,0.0009477382],"genre_scores_gemma":[0.2917311,0.001492732,0.703656,0.00008498815,0.00009955518,0.0000519934,0.0001389044,0.0001658814,0.002578846],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.0007743787,"threshold_uncertainty_score":0.003138065,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W631860307","doi":"10.1007/978-3-0348-8217-0","title":"Wavelet Transforms and Localization Operators","year":2002,"lang":"en","type":"book","venue":"Birkhäuser Basel eBooks","topic":"Image and Signal Denoising Methods","field":"Computer Science","cited_by":282,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"York University","funders":"","keywords":"Wavelet; Center (category theory); Library science; Computer science; Artificial intelligence","authors":[{"name":"M. W. Wong","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.02071423445634275,"gpt":0.2327164896491705,"spread":0.2120022551928278,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002657745,0.001002945,0.0004701842,0.001283608,0.0004470893,0.001761909,0.0003872797,0.0006743476,0.01915695],"category_scores_gemma":[0.0008672394,0.0004161141,0.0003942192,0.00195958,0.001211051,0.002626158,0.0007888392,0.001980812,0.009504715],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004716273,"about_ca_system_score_gemma":0.0004417491,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005270593,"about_ca_topic_score_gemma":0.0007026075,"domain_scores_codex":[0.9998011,0.0000241041,0.00001003729,0.00004214329,0.0001067318,0.00001586594],"domain_scores_gemma":[0.9998237,0.00006486689,0.00001254396,0.00003335227,0.00004956849,0.00001595109],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00002325233,0.00002141339,0.00009910683,0.000235344,0.00001195832,0.0001156565,0.0003268422,0.001491407,0.004644889,0.5718147,0.1294944,0.2917211],"study_design_scores_gemma":[0.000006013319,0.00002395039,0.0004058221,0.0001055077,0.000007630965,0.0004145306,0.0001168876,0.004259721,0.001508742,0.3432019,0.6499349,0.00001430442],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"other","genre_gemma":"methods","genre_scores_codex":[0.005710421,0.1027162,0.4050606,0.005847819,0.008153036,0.00005686569,0.0005455575,0.001585432,0.470324],"genre_scores_gemma":[0.05744406,0.08039864,0.1160348,0.001537384,0.004254289,0.0001293965,0.001174881,0.001096678,0.7379299],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.01915695,"threshold_uncertainty_score":0.06408644,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2014940628","doi":"10.1016/j.media.2011.04.003","title":"New methods for MRI denoising based on sparseness and self-similarity","year":2011,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Image and Signal Denoising Methods","field":"Computer Science","cited_by":280,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"McGill University; Montreal Neurological Institute and Hospital","funders":"Canadian Institutes of Health Research","keywords":"Thresholding; Artificial intelligence; Noise reduction; Pattern recognition (psychology); Similarity (geometry); Image denoising; Computer science; Exploit; Filter (signal processing); Discrete cosine transform; USable; Invariant (physics); Mathematics; Computer vision; Image (mathematics)","authors":[{"name":"José V. Manjón","is_ca":false},{"name":"Pierrick Coupé","is_ca":true},{"name":"Antoni Buades","is_ca":false},{"name":"D. Louis Collins","is_ca":true},{"name":"Montserrat Robles","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.03962570295564427,"gpt":0.3544762924274026,"spread":0.3148505894717584,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001398536,0.0008000778,0.0009425895,0.001486843,0.000296505,0.0007939394,0.00122844,0.001404119,0.001632035],"category_scores_gemma":[0.002777698,0.0005597066,0.001074992,0.0009212479,0.0008884367,0.001764564,0.001207012,0.001781871,0.0007971047],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003429122,"about_ca_system_score_gemma":0.000403757,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003866534,"about_ca_topic_score_gemma":0.001097943,"domain_scores_codex":[0.9993655,0.0001253705,0.00004162845,0.00009011455,0.0003547228,0.0000226358],"domain_scores_gemma":[0.9988052,0.0004568368,0.0001136738,0.0001591662,0.000403403,0.0000616673],"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":[0.0002322328,0.0002149827,0.0006829845,0.0008370241,0.000335879,0.0001961289,0.000335823,0.07602527,0.1361212,0.1425899,0.006712393,0.6357162],"study_design_scores_gemma":[0.00004138133,0.000103842,0.000521205,0.00004162836,0.00008090365,0.0006321406,0.00003079387,0.920195,0.02767496,0.0374312,0.01318317,0.00006381216],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.00140161,0.0004515028,0.9974909,0.00007231627,0.00006785359,0.00001121765,0.00001027656,0.00007141299,0.0004230146],"genre_scores_gemma":[0.02548589,0.001465101,0.9678211,0.0001382671,0.0002970462,0.00007097454,0.00009150351,0.0001216805,0.004508467],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.001632035,"threshold_uncertainty_score":0.007396221,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2109876342","doi":"10.1109/42.974934","title":"Retrospective correction of MR intensity inhomogeneity by information minimization","year":2001,"lang":"en","type":"article","venue":"IEEE Transactions on Medical Imaging","topic":"Image and Signal Denoising Methods","field":"Computer Science","cited_by":267,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"","funders":"University of Manchester; University of South Florida; McGill University; Massachusetts General Hospital","keywords":"Preprocessor; Computer science; Intensity (physics); Image (mathematics); Multiplicative function; Minification; Artificial intelligence; Inverse; Algorithm; Computer vision; Pattern recognition (psychology); Mathematics; Physics; Optics","authors":[{"name":"B. Likar","is_ca":false},{"name":"Max A. Viergever","is_ca":false},{"name":"F. Pernuš","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.01020775673917881,"gpt":0.2619125049483601,"spread":0.2517047482091813,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001204397,0.000692987,0.0009654674,0.0006344098,0.0002631543,0.0005803898,0.0009260945,0.0008547332,0.000544605],"category_scores_gemma":[0.003598491,0.0005349689,0.0006612061,0.0005330116,0.0006696992,0.00110263,0.0007433596,0.0008217898,0.0003608139],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003576369,"about_ca_system_score_gemma":0.0006839028,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009332827,"about_ca_topic_score_gemma":0.0009314353,"domain_scores_codex":[0.9994961,0.0001208935,0.00002844499,0.0001101902,0.0002167187,0.00002763767],"domain_scores_gemma":[0.9988995,0.000459952,0.0002028266,0.0002089894,0.0002025546,0.00002627918],"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":[0.0003043347,0.00006538451,0.00141197,0.0005123675,0.0002245481,0.000265205,0.0002856341,0.3930237,0.1391907,0.02456897,0.002740458,0.4374067],"study_design_scores_gemma":[0.00001739263,0.0001133666,0.0006998033,0.00002811646,0.00006007629,0.0005021703,0.0000214933,0.9437261,0.04150129,0.008223266,0.005062532,0.00004443673],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.004358171,0.0003096334,0.9948715,0.00008327735,0.00001861766,0.000007521949,0.00001133181,0.0001449375,0.0001950135],"genre_scores_gemma":[0.1589345,0.001015457,0.8373563,0.0001176169,0.0001069729,0.00004836236,0.0001687301,0.0002676115,0.001984444],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.001204397,"threshold_uncertainty_score":0.006369531,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W3140715649","doi":"10.1109/tce.2005.1561853","title":"Color filter arrays: design and performance analysis","year":2005,"lang":"en","type":"article","venue":"IEEE Transactions on Consumer Electronics","topic":"Image and Signal Denoising Methods","field":"Computer Science","cited_by":258,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Toronto","funders":"","keywords":"Demosaicing; RGB color model; Color filter array; Artificial intelligence; Computer vision; Pipeline (software); Color gel; Computer science; Bayer filter; Process (computing); Color image; Filter (signal processing); Image processing; Image (mathematics); Materials science","authors":[{"name":"Konstantinos N. Plataniotis","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.02151520278883265,"gpt":0.2578035988823592,"spread":0.2362883960935265,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007323296,0.0007070252,0.0003524386,0.0005768382,0.0002600848,0.0008474818,0.0004055131,0.0007963307,0.003108826],"category_scores_gemma":[0.00198509,0.0002723805,0.0003430201,0.0007768998,0.0002812878,0.0006265909,0.0002709016,0.0004543378,0.001662648],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000862305,"about_ca_system_score_gemma":0.0005553172,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002045607,"about_ca_topic_score_gemma":0.001421536,"domain_scores_codex":[0.999288,0.0001541518,0.00002770057,0.0000953618,0.0003838367,0.00005096295],"domain_scores_gemma":[0.9987659,0.0004694786,0.0001399341,0.00008452424,0.0005136891,0.00002656251],"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":[0.0005043713,0.0001034584,0.002503605,0.0004951787,0.0001289623,0.0001817459,0.0002253017,0.2255487,0.1816087,0.02930932,0.004313736,0.5550768],"study_design_scores_gemma":[0.000026098,0.0003456869,0.001598423,0.00004839107,0.00005580571,0.0006094318,0.0000382261,0.8450081,0.1326082,0.004273777,0.01533354,0.00005431361],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.005187887,0.0005472712,0.991184,0.00006804288,0.00002189519,0.00003696415,0.00003075192,0.000330335,0.002592979],"genre_scores_gemma":[0.3003906,0.002475448,0.6890648,0.0001494541,0.0001211549,0.0002016962,0.0001979683,0.0002024374,0.007196384],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.003108826,"threshold_uncertainty_score":0.01040006,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2034671455","doi":"10.1080/01431160050030592","title":"Destriping multisensor imagery with moment matching","year":2000,"lang":"en","type":"article","venue":"International Journal of Remote Sensing","topic":"Image and Signal Denoising Methods","field":"Computer Science","cited_by":255,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"Golder Associates (Canada); University of Toronto","funders":"Delta Waterfowl; University of Toronto","keywords":"Histogram; Outlier; Histogram matching; Moment (physics); Artificial intelligence; Matching (statistics); Computer science; Offset (computer science); Computer vision; Histogram equalization; Pattern recognition (psychology); Mathematics; Statistics; Image (mathematics)","authors":[{"name":"F. L. Gadallah","is_ca":true},{"name":"F. Csillag","is_ca":true},{"name":"Eric Smith","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.01632281076912038,"gpt":0.2835757035005704,"spread":0.26725289273145,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009987621,0.0006712222,0.001007842,0.001573651,0.0004086997,0.0009381871,0.001096881,0.00102859,0.001908384],"category_scores_gemma":[0.003121158,0.0005837476,0.00118554,0.001587078,0.000567094,0.001727922,0.001529614,0.0009988681,0.001446217],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004666657,"about_ca_system_score_gemma":0.0005120765,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001043546,"about_ca_topic_score_gemma":0.00132905,"domain_scores_codex":[0.9992564,0.0001041488,0.00004579661,0.0001815431,0.0003474159,0.00006460617],"domain_scores_gemma":[0.9991035,0.0002390839,0.0001448168,0.00029541,0.0001801864,0.00003698985],"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":[0.0004027844,0.000108858,0.001066616,0.0001814796,0.0001381233,0.0001376735,0.0001973474,0.0488546,0.130783,0.01000068,0.001873972,0.8062549],"study_design_scores_gemma":[0.00003520397,0.0001842909,0.003553279,0.00002351125,0.00007023408,0.0005786975,0.00008199457,0.8389646,0.1335715,0.01444072,0.008423192,0.00007274646],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.009172218,0.00008970367,0.9892243,0.00004227993,0.00003025653,0.00003503204,0.00002912413,0.0008723913,0.0005047889],"genre_scores_gemma":[0.09701011,0.0001532901,0.9009847,0.0000473869,0.00004329395,0.00006116318,0.0001844446,0.0001999913,0.001315662],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.001908384,"threshold_uncertainty_score":0.006384194,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2045105614","doi":"10.1016/j.media.2010.03.001","title":"Robust Rician noise estimation for MR images","year":2010,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Image and Signal Denoising Methods","field":"Computer Science","cited_by":237,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"Montreal Neurological Institute and Hospital; McGill University","funders":"Canadian Institutes of Health Research","keywords":"Robustness (evolution); Computer science; Artificial intelligence; Estimator; Gaussian noise; Noise (video); Rician fading; Ghosting; Wavelet; Noise reduction; Pattern recognition (psychology); Computer vision; Mathematics; Algorithm; Statistics; Image (mathematics); Fading","authors":[{"name":"Pierrick Coupé","is_ca":true},{"name":"José V. Manjón","is_ca":false},{"name":"Elias Gedamu","is_ca":true},{"name":"Douglas L. Arnold","is_ca":true},{"name":"Montserrat Robles","is_ca":false},{"name":"D. Louis Collins","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.01805896654669985,"gpt":0.3059172455766335,"spread":0.2878582790299337,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00113174,0.0007575289,0.0007908986,0.0009945598,0.0002052774,0.0006887292,0.0007036311,0.001168161,0.001267549],"category_scores_gemma":[0.006092091,0.0005317489,0.0006693766,0.000722301,0.0007166266,0.001056097,0.0008591351,0.00104343,0.0008160713],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004181062,"about_ca_system_score_gemma":0.0006288619,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001632447,"about_ca_topic_score_gemma":0.001981447,"domain_scores_codex":[0.999373,0.0002071789,0.000039897,0.0001083006,0.0002289359,0.00004257984],"domain_scores_gemma":[0.999027,0.0004340986,0.0001269354,0.0001900099,0.000196411,0.00002557271],"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":[0.0004940674,0.00008774143,0.0006672805,0.0005030647,0.0002223815,0.0001658354,0.0001939598,0.3451545,0.1198188,0.04812074,0.00649531,0.4780763],"study_design_scores_gemma":[0.0000149471,0.00004615461,0.0005642329,0.00002366478,0.00005326719,0.0001530646,0.00001779308,0.9476122,0.03025822,0.01690656,0.004320373,0.00002956541],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.003187388,0.0003484787,0.9958401,0.0001078266,0.00001631096,0.00000730377,0.00002254808,0.000163874,0.0003062212],"genre_scores_gemma":[0.1706288,0.001797572,0.8214508,0.0002196451,0.0002015309,0.00007797078,0.0003809305,0.0003854127,0.004857285],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001632447,"threshold_uncertainty_score":0.00598532,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2091659514","doi":"10.1109/tip.2006.888341","title":"Adaptive Directional Lifting-Based Wavelet Transform for Image Coding","year":2007,"lang":"en","type":"article","venue":"IEEE Transactions on Image Processing","topic":"Image and Signal Denoising Methods","field":"Computer Science","cited_by":218,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"McMaster University","funders":"","keywords":"Artificial intelligence; Wavelet transform; Computer vision; Lifting scheme; Wavelet; Computer science; Second-generation wavelet transform; Transform coding; Pixel; Discrete wavelet transform; Image resolution; Coding (social sciences); Stationary wavelet transform; Mathematics; Pattern recognition (psychology); Image (mathematics); Discrete cosine transform","authors":[{"name":"Wenpeng Ding","is_ca":false},{"name":"Feng Wu","is_ca":false},{"name":"Xiaolin Wu","is_ca":true},{"name":"Shipeng Li","is_ca":false},{"name":"Houqiang Li","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.02839448410608021,"gpt":0.3048546841069855,"spread":0.2764602000009053,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002184571,0.0003503766,0.0002516329,0.0004676601,0.0001557781,0.000357955,0.0003652852,0.0003741318,0.001193469],"category_scores_gemma":[0.0006138123,0.000120542,0.0002910785,0.0007715388,0.0002527575,0.0004802782,0.0004351145,0.0006250928,0.0005785832],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002192319,"about_ca_system_score_gemma":0.0002535417,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004710258,"about_ca_topic_score_gemma":0.0005179128,"domain_scores_codex":[0.9998609,0.00002702781,0.00000845841,0.00001613304,0.00007449746,0.00001294762],"domain_scores_gemma":[0.9998778,0.00003174471,0.00001402766,0.00003207856,0.00003605599,0.00000825301],"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":[0.000148065,0.00005895694,0.0004647969,0.0002478506,0.00002169294,0.0002485461,0.0001169111,0.05009619,0.2516882,0.07558744,0.005005496,0.6163158],"study_design_scores_gemma":[0.00003233566,0.0001556817,0.000658292,0.00005039147,0.00002302294,0.0006351078,0.00002504599,0.8791407,0.07197547,0.02023731,0.02702551,0.00004123373],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.008968479,0.0006488197,0.9881619,0.0001111885,0.0000750093,0.00002966201,0.0000401229,0.0001717008,0.001793127],"genre_scores_gemma":[0.1963863,0.00183783,0.7974892,0.0001863829,0.0001540746,0.0001251948,0.0002371951,0.00006176527,0.003522076],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001193469,"threshold_uncertainty_score":0.003992558,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2029860284","doi":"10.1118/1.1513158","title":"A framework for noise‐power spectrum analysis of multidimensional images","year":2002,"lang":"en","type":"article","venue":"Medical Physics","topic":"Image and Signal Denoising Methods","field":"Computer Science","cited_by":205,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"Robarts Clinical Trials; Princess Margaret Cancer Centre; University Health Network","funders":"National Cancer Institute; Canadian Institutes of Health Research; National Institutes of Health","keywords":"Normalization (sociology); Spectral density; Projection (relational algebra); Optics; Filter (signal processing); Noise (video); Mathematics; Algorithm; Computer science; Physics; Image (mathematics); Artificial intelligence; Computer vision","authors":[{"name":"Jeffrey H. Siewerdsen","is_ca":true},{"name":"Ian A. Cunningham","is_ca":true},{"name":"David A. Jaffray","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.02709273379210067,"gpt":0.3091282892141698,"spread":0.2820355554220692,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003921868,0.001506416,0.001147274,0.002724401,0.0008409334,0.003005519,0.003474596,0.001604302,0.002597687],"category_scores_gemma":[0.00616767,0.0007965538,0.001626262,0.001596449,0.004068509,0.002632435,0.002754735,0.002749362,0.001012784],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001534121,"about_ca_system_score_gemma":0.001614173,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001828448,"about_ca_topic_score_gemma":0.001173643,"domain_scores_codex":[0.9977722,0.0007117387,0.0001606778,0.0003674589,0.0008769426,0.0001109161],"domain_scores_gemma":[0.9971139,0.00137824,0.0003080537,0.0006509981,0.0004454903,0.0001031457],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00005881347,0.0001258982,0.0005988302,0.0005557242,0.0001034487,0.0003783294,0.0004374533,0.1240376,0.06259558,0.7263002,0.001552139,0.08325598],"study_design_scores_gemma":[0.00001502422,0.0001244345,0.0007281526,0.0001162487,0.00002111209,0.0002988971,0.0001624951,0.6230237,0.0123516,0.3508437,0.01221546,0.00009915078],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0003926297,0.00007028836,0.9989793,0.00003419735,0.00001197003,0.00002478303,0.00002131933,0.00005839271,0.0004070959],"genre_scores_gemma":[0.03893339,0.0005277471,0.9584263,0.00008514099,0.00009644152,0.0005383454,0.0001510891,0.0001289868,0.001112623],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.003921868,"threshold_uncertainty_score":0.02074105,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2147176572","doi":"10.1109/83.892442","title":"Multiscale MAP filtering of SAR images","year":2001,"lang":"en","type":"article","venue":"IEEE Transactions on Image Processing","topic":"Image and Signal Denoising Methods","field":"Computer Science","cited_by":197,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Université de Sherbrooke","funders":"","keywords":"Wavelet; Artificial intelligence; Synthetic aperture radar; Filter (signal processing); Multiplicative noise; Maximum a posteriori estimation; Probability density function; Pattern recognition (psychology); Mathematics; Computer science; Radar imaging; Noise (video); Wavelet transform; Computer vision; Algorithm; Radar; Image (mathematics); Statistics; Transmission (telecommunications)","authors":[{"name":"Samuel Foucher","is_ca":true},{"name":"G.B. Bénié","is_ca":true},{"name":"J.-M. Boucher","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.02296851443374905,"gpt":0.2917137893126666,"spread":0.2687452748789175,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002839151,0.000298227,0.0003702764,0.0007290975,0.0001443917,0.0005173321,0.0002036871,0.0003354452,0.001184501],"category_scores_gemma":[0.001142885,0.0001653222,0.000456639,0.0006184475,0.0002034171,0.0005233939,0.000407457,0.0002771903,0.0003896596],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001954571,"about_ca_system_score_gemma":0.0001832888,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001240441,"about_ca_topic_score_gemma":0.001079897,"domain_scores_codex":[0.9998497,0.00001868844,0.000006938034,0.00003674745,0.0000691178,0.00001887076],"domain_scores_gemma":[0.9998291,0.00005035997,0.00002422971,0.00003325443,0.00004987427,0.00001331359],"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":[0.0002742673,0.00006218165,0.001564828,0.0002719351,0.0001365127,0.0003289896,0.0002356543,0.1595512,0.1880964,0.04225823,0.003914583,0.6033053],"study_design_scores_gemma":[0.00001229277,0.00008555267,0.006424181,0.00001691298,0.00004179704,0.0002024641,0.00004540991,0.9410096,0.02402401,0.01933459,0.008779228,0.00002391438],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06430279,0.0005260715,0.9324073,0.0001352926,0.00006220418,0.00002246726,0.0001635156,0.0004756793,0.001904649],"genre_scores_gemma":[0.6376617,0.001654261,0.3541169,0.00008785206,0.0002146645,0.00006380685,0.0005500718,0.0001720167,0.005478706],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001240441,"threshold_uncertainty_score":0.003962517,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W1998272579","doi":"10.1109/tsp.2004.826175","title":"Efficient Architectures for 1-D and 2-D Lifting-Based Wavelet Transforms","year":2004,"lang":"en","type":"article","venue":"IEEE Transactions on Signal Processing","topic":"Image and Signal Denoising Methods","field":"Computer Science","cited_by":187,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Alberta","funders":"","keywords":"Datapath; Computer science; Parallel computing; Discrete wavelet transform; Interleaving; Wavelet; Lifting scheme; Algorithm; Computer hardware; Wavelet transform; Artificial intelligence","authors":[{"name":"H. Liao","is_ca":true},{"name":"Mrinal Mandal","is_ca":true},{"name":"B.F. Cockburn","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.02008979188986759,"gpt":0.2756101052116265,"spread":0.2555203133217589,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003473251,0.0004771934,0.0003298733,0.0005347434,0.0002796348,0.0005826931,0.0007413809,0.0005028112,0.003695607],"category_scores_gemma":[0.0009440603,0.0003446963,0.0003212454,0.0006048437,0.0002595811,0.001081501,0.0005856828,0.000529645,0.001221041],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003165531,"about_ca_system_score_gemma":0.0004745771,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005589307,"about_ca_topic_score_gemma":0.001304343,"domain_scores_codex":[0.9997724,0.00002391036,0.00002153504,0.00003389013,0.0001093499,0.00003884444],"domain_scores_gemma":[0.9997603,0.00006187496,0.00002766813,0.00007017862,0.00006623506,0.00001366225],"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":[0.0004406525,0.0001199608,0.0009177659,0.0002744967,0.00003298187,0.0002592597,0.000179762,0.07369374,0.2151861,0.08485071,0.007280552,0.6167639],"study_design_scores_gemma":[0.0001512233,0.0003698917,0.001647082,0.0000503053,0.00004208572,0.0005365704,0.00005448758,0.8539807,0.08262505,0.02807815,0.03240773,0.00005677643],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.03007621,0.0004938317,0.9642933,0.0001452371,0.000082495,0.00006414297,0.00008462262,0.001043234,0.003716907],"genre_scores_gemma":[0.3286946,0.0009299312,0.6641073,0.0001250418,0.0001014912,0.0002179327,0.0005698728,0.00009103528,0.00516283],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.003695607,"threshold_uncertainty_score":0.01236302,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2128480875","doi":"10.1109/tmi.2007.892519","title":"Weighted Fourier Series Representation and Its Application to Quantifying the Amount of Gray Matter","year":2007,"lang":"en","type":"article","venue":"IEEE Transactions on Medical Imaging","topic":"Image and Signal Denoising Methods","field":"Computer Science","cited_by":182,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"McGill University; Montreal Neurological Institute and Hospital","funders":"University of Wisconsin-Madison","keywords":"Smoothing; Residual; Representation (politics); Algorithm; Fourier series; Computer science; Mathematics; Fourier transform; Series (stratigraphy); Applied mathematics; Artificial intelligence; Mathematical analysis; Statistics","authors":[{"name":"Moo K. Chung","is_ca":false},{"name":"Kim M. Dalton","is_ca":false},{"name":"Li Shen","is_ca":false},{"name":"Alan C. Evans","is_ca":true},{"name":"Richard J. Davidson","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.02412637705173458,"gpt":0.3293937782359823,"spread":0.3052674011842477,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007228763,0.0006247665,0.0003405568,0.00135714,0.0001995053,0.0006992205,0.0006294817,0.0005668844,0.002279413],"category_scores_gemma":[0.002301737,0.0001656656,0.0005291742,0.001203578,0.0006747562,0.001026818,0.0005122157,0.000578683,0.0006832793],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002161723,"about_ca_system_score_gemma":0.0003527208,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000836025,"about_ca_topic_score_gemma":0.0005429627,"domain_scores_codex":[0.9997495,0.00006186192,0.00001358962,0.00004347356,0.0001113092,0.00002027988],"domain_scores_gemma":[0.9995523,0.0001673092,0.00006695097,0.00009522305,0.0001016643,0.00001656159],"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":[0.0001248619,0.00006764518,0.001587299,0.0002609854,0.00008010527,0.0005266311,0.0002978955,0.200467,0.1015033,0.2658665,0.005007432,0.4242104],"study_design_scores_gemma":[0.000008283476,0.00006624066,0.001088526,0.0000192039,0.00002503713,0.0006079238,0.00005227071,0.8993898,0.01286015,0.07653553,0.009316427,0.00003057365],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.006967049,0.0001511702,0.9912549,0.0000894754,0.00003312311,0.00001168293,0.00005373962,0.0001796139,0.001259243],"genre_scores_gemma":[0.2069875,0.0009947271,0.7871046,0.0001100704,0.0002195628,0.00010356,0.0003308833,0.0002709351,0.003878197],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.002279413,"threshold_uncertainty_score":0.007625341,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2159509402","doi":"10.1109/tgrs.2003.821885","title":"Homomorphic wavelet-based statistical despeckling of SAR images","year":2004,"lang":"en","type":"article","venue":"IEEE Transactions on Geoscience and Remote Sensing","topic":"Image and Signal Denoising Methods","field":"Computer Science","cited_by":180,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"","funders":"Canadian Space Agency","keywords":"Speckle pattern; Wavelet; Filter (signal processing); Artificial intelligence; Speckle noise; Computer science; Pattern recognition (psychology); Gamma distribution; Mathematics; Synthetic aperture radar; Generalized gamma distribution; Smoothing; Algorithm; Computer vision; Statistics","authors":[{"name":"S. Solbø","is_ca":false},{"name":"Torbjørn Eltoft","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.01946907408372105,"gpt":0.2680010398918395,"spread":0.2485319658081185,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006663101,0.0004202017,0.0004498259,0.000703998,0.0001771465,0.0004905611,0.000508368,0.0004856847,0.00073304],"category_scores_gemma":[0.002025012,0.0002444634,0.0006264067,0.0005966873,0.0004566525,0.0009135442,0.0003646041,0.000536783,0.0005065273],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000195929,"about_ca_system_score_gemma":0.0005420783,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006162708,"about_ca_topic_score_gemma":0.00132492,"domain_scores_codex":[0.9996598,0.00007413934,0.00001892704,0.00006085231,0.0001619675,0.00002429043],"domain_scores_gemma":[0.9994023,0.0002317065,0.00007123256,0.0001119035,0.0001656099,0.00001727572],"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":[0.0003253414,0.00009895685,0.001270393,0.0003614238,0.0001436292,0.000253266,0.0001414708,0.1797172,0.130459,0.02928861,0.001813896,0.6561267],"study_design_scores_gemma":[0.00002217221,0.0001245252,0.002412256,0.00001879312,0.00004836381,0.0005630478,0.00005302565,0.8810231,0.09768963,0.01302822,0.004979413,0.00003741359],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01408269,0.000161082,0.9848741,0.00004971573,0.00002477332,0.00001481555,0.00003936335,0.0001934781,0.0005599629],"genre_scores_gemma":[0.1441685,0.0006923589,0.8519098,0.0001204975,0.00006729063,0.00005558961,0.0003379105,0.0001466811,0.002501483],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00073304,"threshold_uncertainty_score":0.003523827,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2033400894","doi":"10.1109/lsp.2003.811586","title":"Multiwavelets denoising using neighboring coefficients","year":2003,"lang":"en","type":"article","venue":"IEEE Signal Processing Letters","topic":"Image and Signal Denoising Methods","field":"Computer Science","cited_by":171,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Concordia University","funders":"","keywords":"Noise reduction; Thresholding; Wavelet; Pattern recognition (psychology); Artificial intelligence; Video denoising; Computer science; Extension (predicate logic); Term (time); Image denoising; Wavelet transform; Mathematics; Step detection; Algorithm; Computer vision; Image (mathematics)","authors":[{"name":"G.Y. Chen","is_ca":true},{"name":"Tien D. Bui","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.03591050733372826,"gpt":0.288264375172295,"spread":0.2523538678385668,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001271021,0.000847332,0.001043887,0.0008944993,0.0002686191,0.0007529262,0.0005286688,0.00109419,0.001646848],"category_scores_gemma":[0.002889612,0.0004114261,0.001033689,0.0009861022,0.000497442,0.002065953,0.0006975613,0.001107191,0.0008473409],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002094161,"about_ca_system_score_gemma":0.000273795,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004458347,"about_ca_topic_score_gemma":0.001149717,"domain_scores_codex":[0.999446,0.00009418222,0.00003440047,0.0001297197,0.0002538374,0.00004176872],"domain_scores_gemma":[0.9987325,0.0004996372,0.0001231339,0.0002600485,0.0003232771,0.0000614247],"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":[0.0005737396,0.000319755,0.002481141,0.0004350647,0.000221596,0.0002994153,0.0001796675,0.08508535,0.407887,0.009518589,0.002237405,0.4907613],"study_design_scores_gemma":[0.00003878652,0.0002922174,0.002018177,0.00003225491,0.0001352661,0.0005468788,0.00004802664,0.7642121,0.2235644,0.005429519,0.003645956,0.00003647283],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.07718746,0.0009011495,0.9193593,0.0001440276,0.00016608,0.00003489813,0.00004543026,0.0003230576,0.001838424],"genre_scores_gemma":[0.3564553,0.001620025,0.63592,0.0001657921,0.0001739756,0.00005541336,0.0002963088,0.0003466645,0.004966585],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.001646848,"threshold_uncertainty_score":0.006721914,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W3127794790","doi":"10.1016/j.rse.2021.112632","title":"Long time-series NDVI reconstruction in cloud-prone regions via spatio-temporal tensor completion","year":2021,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Image and Signal Denoising Methods","field":"Computer Science","cited_by":170,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Toronto","funders":"National Key Research and Development Program of China; National Natural Science Foundation of China","keywords":"Remote sensing; Normalized Difference Vegetation Index; Cloud computing; Series (stratigraphy); Computer science; Time series; Environmental science; Geology; Climate change","authors":[{"name":"Dong Chu","is_ca":false},{"name":"Huanfeng Shen","is_ca":false},{"name":"Xiaobin Guan","is_ca":true},{"name":"Jing M. Chen","is_ca":true},{"name":"Xinghua Li","is_ca":false},{"name":"Jie Li","is_ca":false},{"name":"Liangpei Zhang","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.01860957977906524,"gpt":0.2320983622633231,"spread":0.2134887824842578,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003882809,0.0005301734,0.0002890338,0.0004732765,0.0002261595,0.0005857976,0.000475137,0.0004730054,0.0007629861],"category_scores_gemma":[0.0009486706,0.0002240418,0.0005411149,0.0006332335,0.000315439,0.0008057024,0.0003673508,0.0007289203,0.0003063041],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002944138,"about_ca_system_score_gemma":0.0009163212,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01246366,"about_ca_topic_score_gemma":0.01288174,"domain_scores_codex":[0.9998952,0.0000173623,0.000006921973,0.00002867007,0.00003068036,0.00002121311],"domain_scores_gemma":[0.9996881,0.00007213527,0.00005773811,0.00005576702,0.0000908108,0.00003545715],"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":[0.0007603219,0.0003478631,0.01574379,0.0002665123,0.000209422,0.0006039097,0.0003734287,0.5177689,0.2036174,0.0102591,0.005041903,0.2450076],"study_design_scores_gemma":[0.000005743852,0.00001231508,0.0025311,0.000004209813,0.00001151893,0.00003675425,0.00003340244,0.989899,0.005971582,0.0009695636,0.0005135385,0.00001125422],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.4035023,0.0004532979,0.5922597,0.0005475873,0.0001234207,0.00003478976,0.000709206,0.0009401575,0.001429662],"genre_scores_gemma":[0.8172853,0.0005032159,0.1777545,0.00005124048,0.00005518306,0.0000249589,0.001335874,0.0001845874,0.002805163],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01246366,"threshold_uncertainty_score":0.02478224,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2100218807","doi":"10.1109/tsp.2009.2028972","title":"A General Description of Linear Time-Frequency Transforms and Formulation of a Fast, Invertible Transform That Samples the Continuous S-Transform Spectrum Nonredundantly","year":2009,"lang":"en","type":"article","venue":"IEEE Transactions on Signal Processing","topic":"Image and Signal Denoising Methods","field":"Computer Science","cited_by":165,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Foothills Medical Centre; University of Calgary","funders":"","keywords":"Algorithm; Discrete Fourier transform (general); Harmonic wavelet transform; Fourier transform; Fractional Fourier transform; Constant Q transform; Non-uniform discrete Fourier transform; S transform; Computer science; Discrete-time Fourier transform; Short-time Fourier transform; Signal processing; Time–frequency analysis; Multidimensional signal processing; Wavelet transform; Hartley transform; Mathematics; Discrete wavelet transform; Fourier analysis; Wavelet; Digital signal processing; Artificial intelligence; Computer vision; Mathematical analysis; Filter (signal processing)","authors":[{"name":"Robert A. Brown","is_ca":true},{"name":"M. Louis Lauzon","is_ca":true},{"name":"Richard Frayne","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.03218999549217932,"gpt":0.2664123395779608,"spread":0.2342223440857815,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009047601,0.001468616,0.0006638175,0.001247904,0.0004836385,0.001824989,0.001641404,0.002248273,0.009034173],"category_scores_gemma":[0.00158085,0.0004586238,0.001247957,0.00216259,0.001435821,0.002634038,0.0008492696,0.002455617,0.007277293],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004645773,"about_ca_system_score_gemma":0.0009666,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001359788,"about_ca_topic_score_gemma":0.001019643,"domain_scores_codex":[0.9994385,0.0001006821,0.00006267646,0.0001359193,0.0002247553,0.0000375318],"domain_scores_gemma":[0.9996432,0.0001527412,0.00004249772,0.00005489222,0.00009278371,0.00001383292],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00004952835,0.0000441155,0.0002294682,0.000521287,0.00003461236,0.0005419584,0.0002591786,0.03761178,0.0195947,0.7775723,0.01462442,0.1489167],"study_design_scores_gemma":[0.00002678837,0.0002075773,0.0004122797,0.0001683163,0.00003482496,0.002648542,0.0001331096,0.2373836,0.008544211,0.4743042,0.2760475,0.00008910742],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0004123929,0.001030917,0.9909875,0.0003097029,0.0002477885,0.00005803759,0.0001929402,0.0001990107,0.006561743],"genre_scores_gemma":[0.03866852,0.006763548,0.9246223,0.0009314936,0.001197599,0.0006249833,0.0009771736,0.0003710949,0.02584334],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.009034173,"threshold_uncertainty_score":0.03022236,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2156291710","doi":"10.1109/tip.2003.814252","title":"Removing the blocking artifacts of block-based DCT compressed images","year":2003,"lang":"en","type":"article","venue":"IEEE Transactions on Image Processing","topic":"Image and Signal Denoising Methods","field":"Computer Science","cited_by":164,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of British Columbia","funders":"","keywords":"Discrete cosine transform; Block (permutation group theory); Computer vision; Artificial intelligence; Smoothing; Pixel; Quantization (signal processing); Classification of discontinuities; Discontinuity (linguistics); Computer science; Blocking (statistics); Blocking effect; Transform coding; Color Cell Compression; Enhanced Data Rates for GSM Evolution; Mathematics; Algorithm; Image compression; Image processing; Image (mathematics)","authors":[{"name":"Ying Luo","is_ca":false},{"name":"Rabab Ward","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.02364186474799729,"gpt":0.2777265679184133,"spread":0.254084703170416,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000230072,0.0004111522,0.0004351365,0.0004795166,0.000171302,0.0003480223,0.000241474,0.0004597363,0.001513298],"category_scores_gemma":[0.001426502,0.000165224,0.0002759368,0.000449148,0.0001718166,0.0003480552,0.0002643699,0.0003863327,0.0006972493],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001176234,"about_ca_system_score_gemma":0.0004072814,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001452293,"about_ca_topic_score_gemma":0.001809955,"domain_scores_codex":[0.999842,0.00001349765,0.000009501128,0.00001217373,0.0001077722,0.00001500731],"domain_scores_gemma":[0.9995498,0.000130983,0.00005171742,0.00007446229,0.0001641989,0.00002879911],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002903625,0.00005833104,0.0007017672,0.0001750353,0.00002643719,0.0002676003,0.00006727334,0.009794652,0.8051566,0.001970309,0.001128422,0.1803632],"study_design_scores_gemma":[0.00006146625,0.000344192,0.007486897,0.00004139699,0.0001157443,0.001686202,0.00006368874,0.2667544,0.7103896,0.001557526,0.01146184,0.00003710293],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4047754,0.001691162,0.5852803,0.0003694376,0.000240296,0.00009051098,0.0002124775,0.001225364,0.006115081],"genre_scores_gemma":[0.6023082,0.002158776,0.3851113,0.0001895137,0.0001668203,0.00006542015,0.0005798946,0.0002305578,0.009189464],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001513298,"threshold_uncertainty_score":0.005062521,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2149825794","doi":"10.1109/tcsvt.2004.837017(410)","title":"Fast and reliable structure-oriented video noise estimation","year":2005,"lang":"en","type":"article","venue":"","topic":"Image and Signal Denoising Methods","field":"Computer Science","cited_by":163,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Ottawa; Concordia University","funders":"","keywords":"Computer science; Variance (accounting); Homogeneity (statistics); Homogeneous; Video quality; Computer vision; Noise (video); Artificial intelligence; Noise measurement; Image quality; Video processing; Image (mathematics); Algorithm; Noise reduction; Mathematics","authors":[{"name":"A. Amer","is_ca":true},{"name":"Éric Dubois","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.008740105259424544,"gpt":0.2578011157459084,"spread":0.2490610104864839,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006259531,0.0005924244,0.0004707203,0.0009068055,0.000187246,0.0006163905,0.0004592897,0.0005400435,0.001046469],"category_scores_gemma":[0.002104278,0.000267919,0.0003477782,0.0005218021,0.0003686081,0.0007171371,0.0004005104,0.0004905253,0.0005086974],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002980747,"about_ca_system_score_gemma":0.0002876365,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007868718,"about_ca_topic_score_gemma":0.0009717108,"domain_scores_codex":[0.9993524,0.0001396702,0.00002614301,0.0001132113,0.0003341798,0.00003442032],"domain_scores_gemma":[0.9992892,0.0002316279,0.00008412321,0.00009062907,0.0002843733,0.00002013746],"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":[0.0005211979,0.00007137615,0.001904795,0.0001290007,0.00006666149,0.0001337196,0.00009537869,0.0537595,0.3746799,0.007081903,0.001937902,0.5596186],"study_design_scores_gemma":[0.00002409734,0.000136259,0.002473552,0.00001482709,0.0000291068,0.0002732932,0.00001993756,0.8293149,0.1628949,0.002220331,0.002573031,0.00002575239],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.02480276,0.0001960609,0.9735579,0.0000388454,0.00002406009,0.00001842978,0.00002429977,0.0004864934,0.0008511726],"genre_scores_gemma":[0.4161571,0.0003102181,0.5800154,0.00004816527,0.00008151928,0.0000488147,0.000174749,0.0001620147,0.003002051],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.001046469,"threshold_uncertainty_score":0.003500819,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2099691301","doi":"10.1109/72.925559","title":"Thresholding neural network for adaptive noise reduction","year":2001,"lang":"en","type":"article","venue":"IEEE Transactions on Neural Networks","topic":"Image and Signal Denoising Methods","field":"Computer Science","cited_by":159,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Thresholding; Artificial neural network; Artificial intelligence; Computer science; Adaptive filter; Noise (video); Noise reduction; Reduction (mathematics); Pattern recognition (psychology); Adaptive learning; Unsupervised learning; Algorithm; Mathematics; Image (mathematics)","authors":[{"name":"Xiao–Ping Zhang","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.04049094824790187,"gpt":0.2826696035139611,"spread":0.2421786552660592,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005358112,0.0003913371,0.0004604435,0.0003173907,0.0002143598,0.0004729737,0.0005994659,0.0007675414,0.001468072],"category_scores_gemma":[0.001552953,0.0001582819,0.0003105107,0.0005710503,0.0004042307,0.0007031099,0.0004069789,0.0007389709,0.0004399058],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003808538,"about_ca_system_score_gemma":0.0003800637,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009982502,"about_ca_topic_score_gemma":0.001152325,"domain_scores_codex":[0.9996787,0.00006449591,0.00001916003,0.00007293758,0.000144314,0.00002029295],"domain_scores_gemma":[0.9997708,0.0001011797,0.00002234354,0.00001847567,0.00007908896,0.000008110094],"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":[0.0001677361,0.00006943969,0.0009835428,0.0005248738,0.0001193979,0.0002752277,0.0001085035,0.3762275,0.04239256,0.09011695,0.0052967,0.4837176],"study_design_scores_gemma":[0.000008145486,0.00003504424,0.0001799026,0.00001969035,0.00002247011,0.0001084082,0.000006891073,0.9787249,0.004371171,0.0116809,0.004833568,0.000008948635],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.005726722,0.001648991,0.9881009,0.0001719027,0.0001511764,0.00002109827,0.00001958068,0.0002132281,0.003946406],"genre_scores_gemma":[0.4893152,0.003575821,0.4926376,0.0005247281,0.0003215163,0.0002492129,0.0002064889,0.0001101549,0.01305925],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001468072,"threshold_uncertainty_score":0.004911184,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2004924126","doi":"10.1109/tmi.2008.929098","title":"Segmentation in Ultrasonic<i>B</i>-Mode Images of Healthy Carotid Arteries Using Mixtures of Nakagami Distributions and Stochastic Optimization","year":2008,"lang":"en","type":"article","venue":"IEEE Transactions on Medical Imaging","topic":"Image and Signal Denoising Methods","field":"Computer Science","cited_by":157,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Université de Montréal; Centre Hospitalier de l’Université de Montréal","funders":"","keywords":"Nakagami distribution; Carotid arteries; Ultrasonic sensor; Ultrasonic imaging; Image segmentation; Segmentation; Biomedical engineering; Artificial intelligence; Computer vision; Radiology; Computer science; Medicine; Cardiology; Algorithm","authors":[{"name":"François Destrempes","is_ca":true},{"name":"Jean Meunier","is_ca":true},{"name":"Marie-France Giroux","is_ca":true},{"name":"Gilles Soulez","is_ca":true},{"name":"Guy Cloutier","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.01774464821256064,"gpt":0.3038225697878452,"spread":0.2860779215752845,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001343816,0.0005521933,0.0007068867,0.000961856,0.0002613146,0.0008021459,0.0004648897,0.0008139958,0.0002932241],"category_scores_gemma":[0.002889988,0.0006108592,0.0007175024,0.0004957918,0.0008353867,0.0006306346,0.0005948129,0.0004607625,0.0002115113],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005001575,"about_ca_system_score_gemma":0.0007906262,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002464313,"about_ca_topic_score_gemma":0.002399938,"domain_scores_codex":[0.99959,0.0001719873,0.00002983679,0.00008198214,0.00008735638,0.00003897457],"domain_scores_gemma":[0.9991565,0.0005249881,0.0001455198,0.00006169822,0.00007952828,0.00003169758],"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":[0.0006222865,0.00007832329,0.004245951,0.0001748176,0.0001018999,0.0002548214,0.0002985316,0.7701892,0.08273189,0.008466774,0.0003654928,0.1324701],"study_design_scores_gemma":[0.00001151208,0.00002592993,0.00113308,0.000005867912,0.0000105815,0.00007305064,0.00001526484,0.9858674,0.009465879,0.003169761,0.0002064017,0.00001531172],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06685137,0.0001455247,0.932519,0.00006197287,0.000005959154,0.00001643637,0.00001515689,0.0002130577,0.0001715682],"genre_scores_gemma":[0.4861968,0.0003238659,0.5121568,0.00005928534,0.00003081514,0.00007569171,0.0001321485,0.0001219455,0.0009026676],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002464313,"threshold_uncertainty_score":0.007106841,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2058936451","doi":"10.1109/tit.2006.872849","title":"Sigma-delta (/spl Sigma//spl Delta/) quantization and finite frames","year":2006,"lang":"en","type":"article","venue":"IEEE Transactions on Information Theory","topic":"Image and Signal Denoising Methods","field":"Computer Science","cited_by":156,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of British Columbia","funders":"","keywords":"Quantization (signal processing); Sigma; Mathematics; Norm (philosophy); Upper and lower bounds; Mean squared error; Delta-sigma modulation; Combinatorics; Physics; Algorithm; Mathematical analysis; Statistics; Quantum mechanics","authors":[{"name":"John J. Benedetto","is_ca":false},{"name":"Alexander M. Powell","is_ca":false},{"name":"Özgür Yılmaz","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.009930336720268619,"gpt":0.2385886236583999,"spread":0.2286582869381313,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008510399,0.0005124447,0.0004960806,0.0005043311,0.000246993,0.0009484026,0.0008267263,0.0006784484,0.002086371],"category_scores_gemma":[0.003741796,0.0002467198,0.0002676703,0.0007900873,0.001007534,0.001156952,0.0008746844,0.001217867,0.0006139266],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006005144,"about_ca_system_score_gemma":0.0004900617,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00111424,"about_ca_topic_score_gemma":0.001297841,"domain_scores_codex":[0.9993229,0.0001813572,0.00005552995,0.0001128001,0.0002849443,0.00004246485],"domain_scores_gemma":[0.999062,0.0004118651,0.0001161079,0.0001625361,0.0002205544,0.00002691175],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.000318227,0.000032991,0.0006084556,0.0003112572,0.00003133446,0.0002437501,0.0002364267,0.1327124,0.04585979,0.4549743,0.002792859,0.3618783],"study_design_scores_gemma":[0.00001484432,0.0001320152,0.0003759454,0.00008735604,0.00001458082,0.00034811,0.00005169715,0.8314122,0.03971132,0.1148465,0.01296568,0.00003976616],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.005738975,0.0008914429,0.9914118,0.00007038398,0.00005603757,0.00001367659,0.00002801712,0.0001252484,0.001664326],"genre_scores_gemma":[0.2527875,0.001803826,0.7391105,0.0001660543,0.0001140399,0.00007994006,0.0001965156,0.00009021273,0.005651464],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002086371,"threshold_uncertainty_score":0.006979585,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2171775321","doi":"10.1109/tip.2004.832920","title":"Primary-Consistent Soft-Decision Color Demosaicking for Digital Cameras (Patent Pending)","year":2004,"lang":"en","type":"article","venue":"IEEE Transactions on Image Processing","topic":"Image and Signal Denoising Methods","field":"Computer Science","cited_by":155,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"McMaster University","funders":"","keywords":"Demosaicing; Artificial intelligence; Color filter array; Computer vision; Color image; Interpolation (computer graphics); Computer science; Sample (material); Primary color; Digital camera; Mathematics; Pattern recognition (psychology); Color gel; Image (mathematics); Image processing","authors":[{"name":"Xiaolin Wu","is_ca":true},{"name":"Ningning Zhang","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.03729791529568903,"gpt":0.2853431932711355,"spread":0.2480452779754465,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001188421,0.0006061092,0.0004648006,0.0008273284,0.0002179237,0.0007936293,0.0007467378,0.0008915735,0.009581529],"category_scores_gemma":[0.002403098,0.0003187337,0.0004711667,0.000734889,0.0005479191,0.0008671518,0.0004197271,0.0009031215,0.003279625],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006564354,"about_ca_system_score_gemma":0.0007413141,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002260365,"about_ca_topic_score_gemma":0.002719765,"domain_scores_codex":[0.9995277,0.00006039082,0.00001935261,0.00007823728,0.0002789515,0.00003550227],"domain_scores_gemma":[0.9993087,0.0001690959,0.00005064326,0.00009704436,0.0003207683,0.0000536994],"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":[0.0002765739,0.00008410327,0.0003047429,0.0003065943,0.00004747912,0.0002569161,0.00005803399,0.01279753,0.0511255,0.02998841,0.02245682,0.8822973],"study_design_scores_gemma":[0.0002921573,0.000695125,0.002711916,0.0001944121,0.000130735,0.00139886,0.00006907644,0.6182102,0.1714975,0.05252598,0.1521079,0.0001661604],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.005155776,0.005190267,0.9763792,0.001018861,0.0006501131,0.0001278057,0.0001700558,0.0007274669,0.01058049],"genre_scores_gemma":[0.1031002,0.007652994,0.8586357,0.0004892286,0.0003760437,0.0001518104,0.000777879,0.000123619,0.02869254],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.009581529,"threshold_uncertainty_score":0.03205341,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2996813151","doi":"10.3390/en13010130","title":"Generating Energy Data for Machine Learning with Recurrent Generative Adversarial Networks","year":2019,"lang":"en","type":"article","venue":"Energies","topic":"Image and Signal Denoising Methods","field":"Computer Science","cited_by":154,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"Western University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Recurrent neural network; Artificial intelligence; Deep learning; Smart grid; Machine learning; Convolutional neural network; Autoregressive integrated moving average; Stability (learning theory); Grid; Energy consumption; Anomaly detection; Data mining; Time series; Artificial neural network","authors":[{"name":"Mohammad Navid Fekri","is_ca":true},{"name":"Ananda Mohon Ghosh","is_ca":true},{"name":"Katarina Grolinger","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.02720047849271696,"gpt":0.2738327244930254,"spread":0.2466322460003084,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001029081,0.0007526166,0.0005311225,0.0003408509,0.0001964657,0.0005348106,0.0008950782,0.0007848809,0.001438717],"category_scores_gemma":[0.003602193,0.0004224239,0.0006679437,0.0004100855,0.0006063631,0.0008734634,0.000845102,0.001921177,0.0004272415],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007239004,"about_ca_system_score_gemma":0.0003816532,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002997592,"about_ca_topic_score_gemma":0.004057033,"domain_scores_codex":[0.9996847,0.0001296322,0.00001460628,0.00007396844,0.0000673071,0.00002974313],"domain_scores_gemma":[0.9988688,0.00079541,0.00007761989,0.0001242959,0.0001075918,0.00002624216],"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":[0.00004055348,0.00002072186,0.0004021735,0.00002087879,0.00001638907,0.00003786469,0.00002077422,0.9754484,0.000878798,0.006859105,0.000734,0.01552038],"study_design_scores_gemma":[0.000001351673,0.000003685108,0.00002928517,0.000001388536,9.232832e-7,0.000004099356,0.000001276233,0.9975889,0.0002844751,0.001967235,0.0001158113,0.000001552583],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.02404471,0.0001909534,0.9729722,0.0002945558,0.00004640592,0.0000515901,0.0002065567,0.0006713237,0.001521701],"genre_scores_gemma":[0.8012077,0.0003359867,0.1927751,0.0003056878,0.00006573279,0.0002436788,0.001232933,0.000199538,0.003633649],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.002997592,"threshold_uncertainty_score":0.005960286,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2137475834","doi":"10.1109/42.952732","title":"Analysis of asymmetry in mammograms via directional filtering with Gabor wavelets","year":2001,"lang":"en","type":"article","venue":"IEEE Transactions on Medical Imaging","topic":"Image and Signal Denoising Methods","field":"Computer Science","cited_by":152,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Bombardier Recreational Products (Canada); Alberta Cancer Foundation; University of Calgary","funders":"","keywords":"Artificial intelligence; Wavelet; Pattern recognition (psychology); Thresholding; Wavelet transform; Mathematics; Principal component analysis; Gabor filter; Multiresolution analysis; Computer vision; Gabor wavelet; Computer science; Filter bank; Filter (signal processing); Discrete wavelet transform; Feature extraction; Image (mathematics)","authors":[{"name":"Ricardo J. Ferrari","is_ca":true},{"name":"Rangaraj M. Rangayyan","is_ca":true},{"name":"J. E. Leo Desautels","is_ca":true},{"name":"A.F. Frere","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.01132122646580772,"gpt":0.274919906669998,"spread":0.2635986802041902,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00100924,0.0003631058,0.0005633321,0.002083043,0.0001618747,0.000427777,0.000214437,0.000252461,0.0009818185],"category_scores_gemma":[0.002762777,0.0002022239,0.0004550681,0.0008192499,0.0002784883,0.0006484113,0.0003486084,0.0003312496,0.0004068999],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000127923,"about_ca_system_score_gemma":0.0001906286,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000366463,"about_ca_topic_score_gemma":0.0004543876,"domain_scores_codex":[0.9995887,0.00008624769,0.00003350578,0.0000539959,0.0001957865,0.00004180397],"domain_scores_gemma":[0.9993,0.0003015978,0.0001025641,0.0001135059,0.0001542137,0.00002807939],"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":[0.0006666335,0.00008176501,0.01174777,0.0002922059,0.0001098293,0.0003509749,0.0001823667,0.005474502,0.4002858,0.00289081,0.0007006266,0.5772167],"study_design_scores_gemma":[0.0001188465,0.001014608,0.1794889,0.0001027496,0.0005704481,0.006362931,0.0005693575,0.382316,0.4045317,0.01244313,0.01231028,0.0001711958],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2024948,0.0006414117,0.7940742,0.0001003982,0.00004042219,0.0001077374,0.0002398324,0.0007436831,0.001557463],"genre_scores_gemma":[0.5494959,0.0009308165,0.4481387,0.00003619038,0.00006135935,0.00008552244,0.0004115881,0.0001254252,0.0007145372],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002083043,"threshold_uncertainty_score":0.005337417,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2112545418","doi":"10.1109/tip.2006.881992","title":"Translation-Invariant Contourlet Transform and Its Application to Image Denoising","year":2006,"lang":"en","type":"article","venue":"IEEE Transactions on Image Processing","topic":"Image and Signal Denoising Methods","field":"Computer Science","cited_by":150,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"McMaster University","funders":"","keywords":"Contourlet; Invariant (physics); Wavelet transform; Filter bank; Noise reduction; Artificial intelligence; Image denoising; Pattern recognition (psychology); Mathematics; Redundancy (engineering); Wavelet; Filter (signal processing); Computer science; Algorithm; Computer vision","authors":[{"name":"R. Esla","is_ca":true},{"name":"Hayder Radha","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.01614987512096629,"gpt":0.2776312431378795,"spread":0.2614813680169132,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004671951,0.0003591045,0.0003067129,0.000519213,0.0001387967,0.000427249,0.0003602435,0.0006791768,0.001005039],"category_scores_gemma":[0.001581345,0.0002019647,0.0004366392,0.0007930532,0.0005761398,0.0005592934,0.0003124123,0.0006705061,0.0002829449],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002065944,"about_ca_system_score_gemma":0.0002770371,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004374981,"about_ca_topic_score_gemma":0.0003987424,"domain_scores_codex":[0.9998456,0.00002737145,0.00001027744,0.00002909067,0.0000764404,0.00001127661],"domain_scores_gemma":[0.9996322,0.0001479949,0.00004371415,0.00006796003,0.00009427518,0.0000137747],"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":[0.0001348255,0.00005892004,0.0007971878,0.0002323569,0.00005906176,0.000446282,0.0002447827,0.1156584,0.1534778,0.1260602,0.0019908,0.6008393],"study_design_scores_gemma":[0.00001186981,0.0001050571,0.0008089572,0.00001673341,0.00003132342,0.0004776648,0.00002933689,0.9077349,0.05467857,0.02496043,0.01112375,0.00002132347],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.007685167,0.000485571,0.9903806,0.0001042186,0.00003950187,0.00001198977,0.000009481506,0.00007831805,0.001205218],"genre_scores_gemma":[0.1808699,0.002922487,0.8121924,0.00008779121,0.0001693736,0.00004954743,0.00007502709,0.00006170365,0.003571713],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.001005039,"threshold_uncertainty_score":0.003362238,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2155311268","doi":"10.1109/tip.2008.2011384","title":"PCA-Based Spatially Adaptive Denoising of CFA Images for Single-Sensor Digital Cameras","year":2009,"lang":"en","type":"article","venue":"IEEE Transactions on Image Processing","topic":"Image and Signal Denoising Methods","field":"Computer Science","cited_by":150,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"McMaster University","funders":"","keywords":"Noise reduction; Artificial intelligence; Computer vision; Computer science; Video denoising; Color filter array; Noise (video); Median filter; Filter (signal processing); Color image; Image sensor; Process (computing); Image processing; Pattern recognition (psychology); Image (mathematics); Color gel; Video processing","authors":[{"name":"Lei Zhang","is_ca":false},{"name":"Xiaolin Wu","is_ca":true},{"name":"David Zhang","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.02891400673190719,"gpt":0.2809607570402935,"spread":0.2520467503083863,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004735186,0.0006036047,0.0005059834,0.0007265852,0.0002428338,0.0003931029,0.0005313934,0.0004600969,0.0006950605],"category_scores_gemma":[0.001566781,0.0002814305,0.0005352719,0.0007650061,0.0003870418,0.0007671433,0.0003145378,0.0005081761,0.0003161718],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003111036,"about_ca_system_score_gemma":0.0003801017,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001222749,"about_ca_topic_score_gemma":0.001880622,"domain_scores_codex":[0.9997029,0.00005615369,0.000013384,0.00005357093,0.0001602745,0.00001372171],"domain_scores_gemma":[0.9995035,0.0001509666,0.00005943236,0.00008687985,0.0001819622,0.0000172718],"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":[0.0002797928,0.00009891124,0.001314552,0.0003498698,0.00009486265,0.000186972,0.000131045,0.07057764,0.2733828,0.01086462,0.002217073,0.640502],"study_design_scores_gemma":[0.00002007229,0.0001501199,0.001805137,0.0000214738,0.00005538781,0.0005304144,0.00003411179,0.8314276,0.158138,0.003252077,0.00451463,0.00005089884],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01299667,0.0003373841,0.9855705,0.00005530273,0.00002854186,0.00001980861,0.00001855611,0.0003216335,0.0006515813],"genre_scores_gemma":[0.2041463,0.000964573,0.7932225,0.00005458209,0.00004669433,0.00004144518,0.00008507712,0.00006666918,0.001372157],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.001222749,"threshold_uncertainty_score":0.00250423,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2100846049","doi":"10.1109/tip.2006.877363","title":"CCD noise removal in digital images","year":2006,"lang":"en","type":"article","venue":"IEEE Transactions on Image Processing","topic":"Image and Signal Denoising Methods","field":"Computer Science","cited_by":150,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"York University; University of Toronto","funders":"","keywords":"Computer vision; Brightness; Artificial intelligence; Noise reduction; Computer science; Noise (video); Image restoration; Filter (signal processing); Image processing; Optics; Image (mathematics); Physics","authors":[{"name":"Hamza Faraji","is_ca":true},{"name":"W. James MacLean","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.01275566478385539,"gpt":0.265026578066734,"spread":0.2522709132828787,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004903703,0.0004202466,0.0005352824,0.0008613577,0.0002925573,0.0006383333,0.000690548,0.000717258,0.0009920814],"category_scores_gemma":[0.001992944,0.0002440892,0.0005380097,0.0007610788,0.0004959995,0.0005753866,0.0006512027,0.0004438429,0.0006017158],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004979368,"about_ca_system_score_gemma":0.0003536804,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001689326,"about_ca_topic_score_gemma":0.001629942,"domain_scores_codex":[0.9993998,0.00006567637,0.00002629558,0.0001055088,0.0003673735,0.00003522988],"domain_scores_gemma":[0.9994504,0.0001259728,0.0000509056,0.0001369669,0.0002159609,0.0000197929],"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":[0.0003763173,0.00009976411,0.002345509,0.0005699394,0.00009536549,0.0003481734,0.000316791,0.07161049,0.3400133,0.01909459,0.003438713,0.561691],"study_design_scores_gemma":[0.00004147927,0.0002038635,0.002896021,0.00005419491,0.00009113808,0.0007846041,0.00008297805,0.6186209,0.3410661,0.008924232,0.0271739,0.00006052664],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03892348,0.0008391362,0.956907,0.0001588522,0.00009612972,0.00004037141,0.00004934093,0.0006359373,0.002349709],"genre_scores_gemma":[0.2835924,0.001310844,0.7080483,0.0003123789,0.0001250665,0.00006533162,0.0002618664,0.0001699858,0.006113807],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001689326,"threshold_uncertainty_score":0.003612816,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W1977472541","doi":"10.1007/s11263-010-0330-1","title":"An Anisotropic Fourth-Order Diffusion Filter for Image Noise Removal","year":2010,"lang":"en","type":"article","venue":"International Journal of Computer Vision","topic":"Image and Signal Denoising Methods","field":"Computer Science","cited_by":145,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Concordia University","funders":"","keywords":"Anisotropic diffusion; Edge-preserving smoothing; Mathematics; Filter (signal processing); Speckle noise; Noise (video); Nonlinear filter; Adaptive filter; Algorithm; Diffusion; Artificial intelligence; Computer vision; Computer science; Filter design; Image (mathematics); Physics","authors":[{"name":"Mohammad Reza Hajiaboli","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.01198180678129496,"gpt":0.3212085976822605,"spread":0.3092267909009655,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004904299,0.0006305703,0.0006543423,0.0007348558,0.0003592073,0.0005193814,0.000570766,0.001047577,0.001730215],"category_scores_gemma":[0.0009037085,0.0002900685,0.0008315017,0.000711727,0.0003278349,0.00073785,0.0003511304,0.0008028276,0.0008254436],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004292056,"about_ca_system_score_gemma":0.0007800976,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00272495,"about_ca_topic_score_gemma":0.004509885,"domain_scores_codex":[0.9997846,0.00003626903,0.00001738913,0.00003720065,0.0001061287,0.00001845363],"domain_scores_gemma":[0.9996146,0.00009861104,0.00002939604,0.00006049868,0.0001725282,0.00002422687],"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":[0.0005196604,0.0001490085,0.0007705831,0.0003911702,0.0001636608,0.0002365096,0.0001349805,0.04947734,0.3545856,0.02110258,0.005204502,0.5672644],"study_design_scores_gemma":[0.00003880733,0.0001287144,0.0009688148,0.00002522331,0.0001485336,0.0006156219,0.00002564726,0.857029,0.1166592,0.004124549,0.02017196,0.00006381532],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01030065,0.0005861832,0.9873759,0.0001449007,0.000114832,0.00001866374,0.00003782755,0.0002830049,0.001138183],"genre_scores_gemma":[0.07767329,0.001281469,0.9119436,0.000112554,0.00008488372,0.00004685578,0.000164411,0.00012356,0.008569368],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00272495,"threshold_uncertainty_score":0.005788088,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W1984487106","doi":"10.1016/j.patcog.2006.04.039","title":"Image denoising with complex ridgelets","year":2006,"lang":"en","type":"article","venue":"Pattern Recognition","topic":"Image and Signal Denoising Methods","field":"Computer Science","cited_by":143,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"Université de Montréal","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Space Agency","keywords":"Complex wavelet transform; Artificial intelligence; Noise reduction; Curvelet; Pattern recognition (psychology); Image denoising; Computer science; Non-local means; Computer vision; Wavelet; Wavelet transform; Mathematics; Video denoising; Filter (signal processing); Discrete wavelet transform; Video processing","authors":[{"name":"G.Y. Chen","is_ca":true},{"name":"Balázs Kégl","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.03552637617486372,"gpt":0.2667984894317006,"spread":0.2312721132568369,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001477374,0.0007540165,0.0007692511,0.0007996865,0.0002205731,0.001083685,0.0005639765,0.001101442,0.002147387],"category_scores_gemma":[0.003251244,0.0005349814,0.0007121239,0.0008170709,0.0008678942,0.0012956,0.001024612,0.001522805,0.00118721],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002802345,"about_ca_system_score_gemma":0.0003338642,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004111879,"about_ca_topic_score_gemma":0.0006425474,"domain_scores_codex":[0.9994434,0.0001417537,0.00003279091,0.0000864077,0.0002632712,0.00003237481],"domain_scores_gemma":[0.9987207,0.000410722,0.0001243293,0.0004106744,0.0002790982,0.00005450588],"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":[0.0006919758,0.0002242441,0.001548159,0.0003448629,0.0002662914,0.0002785454,0.0002322601,0.1488213,0.1978692,0.07517788,0.007483659,0.5670615],"study_design_scores_gemma":[0.00002454762,0.00005990609,0.000661181,0.00001430208,0.00003463728,0.0002371955,0.00001620355,0.939761,0.04188747,0.0129958,0.004284418,0.00002332039],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.00698135,0.0001468779,0.9916048,0.0001220558,0.00006353184,0.00001230138,0.00001854636,0.000221601,0.0008289189],"genre_scores_gemma":[0.1408608,0.0006627856,0.8502736,0.0001863747,0.0001665922,0.00004903091,0.0001516658,0.0002998697,0.007349324],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002147387,"threshold_uncertainty_score":0.007813215,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2110206939","doi":"10.1109/tbme.2008.923140","title":"Speckle Noise Reduction of Medical Ultrasound Images in Complex Wavelet Domain Using Mixture Priors","year":2008,"lang":"en","type":"article","venue":"IEEE Transactions on Biomedical Engineering","topic":"Image and Signal Denoising Methods","field":"Computer Science","cited_by":142,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Queen's University","funders":"","keywords":"Speckle noise; Noise reduction; Wavelet; Speckle pattern; Noise (video); Gaussian noise; Artificial intelligence; Estimator; Mathematics; Maximum a posteriori estimation; Computer science; Thresholding; Algorithm; Pattern recognition (psychology); Image (mathematics); Statistics","authors":[{"name":"Hossein Rabbani","is_ca":false},{"name":"Mansur Vafadust","is_ca":false},{"name":"Purang Abolmaesumi","is_ca":true},{"name":"Saeed Gazor","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.02303769503866289,"gpt":0.2697295235787772,"spread":0.2466918285401143,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001175239,0.0004842747,0.0006005186,0.0006192426,0.0001408942,0.0004486805,0.0004727377,0.0005455537,0.0003553485],"category_scores_gemma":[0.002410988,0.000329014,0.0007224762,0.0004503596,0.0004899003,0.0008524173,0.0006707678,0.0006135017,0.00019931],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002590253,"about_ca_system_score_gemma":0.0003459997,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000877332,"about_ca_topic_score_gemma":0.001144725,"domain_scores_codex":[0.9996767,0.0001007843,0.00001962362,0.0000599562,0.0001259067,0.00001689691],"domain_scores_gemma":[0.9994779,0.0002906132,0.00007117663,0.00006196649,0.00008260051,0.00001583664],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00034733,0.00009760435,0.001847285,0.0002376477,0.0001851639,0.0001830928,0.0001693826,0.5098542,0.1287136,0.01614376,0.001033649,0.3411873],"study_design_scores_gemma":[0.00001090997,0.00003031119,0.0006318911,0.000007828131,0.00002471888,0.00007858025,0.000007398017,0.9843079,0.01150636,0.002869359,0.0005135068,0.00001119038],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01761194,0.0001840386,0.9818015,0.00007313403,0.000005794398,0.000007102174,0.00001123611,0.00008123986,0.0002240111],"genre_scores_gemma":[0.3045952,0.0009193991,0.6926511,0.0001026125,0.00004698436,0.00005329481,0.0001374205,0.0001059387,0.001388086],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001175239,"threshold_uncertainty_score":0.006215334,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2055750192","doi":"10.1016/j.patcog.2004.05.009","title":"Image denoising with neighbour dependency and customized wavelet and threshold","year":2004,"lang":"en","type":"article","venue":"Pattern Recognition","topic":"Image and Signal Denoising Methods","field":"Computer Science","cited_by":140,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Concordia University","funders":"","keywords":"Wavelet; Non-local means; Artificial intelligence; Computer science; Pattern recognition (psychology); Wavelet transform; Stationary wavelet transform; Wavelet packet decomposition; Computer vision; Noise reduction; Second-generation wavelet transform; Image processing; Cascade algorithm; Image quality; Image (mathematics); Mathematics; Image denoising","authors":[{"name":"G.Y. Chen","is_ca":true},{"name":"Tien D. Bui","is_ca":true},{"name":"Adam Krzyżak","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.01966379606932193,"gpt":0.2467388848247654,"spread":0.2270750887554435,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001181089,0.0005789994,0.0007302037,0.0007140362,0.0003443102,0.0008983279,0.0009649033,0.001102519,0.001966308],"category_scores_gemma":[0.002821249,0.0004902011,0.001070028,0.0009433171,0.0007124083,0.001078659,0.001213135,0.001163801,0.0007742532],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004900878,"about_ca_system_score_gemma":0.0007143405,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001467984,"about_ca_topic_score_gemma":0.003072168,"domain_scores_codex":[0.9993874,0.000109567,0.00004684891,0.0001496134,0.0002590103,0.00004761632],"domain_scores_gemma":[0.9992494,0.0001953914,0.00005796413,0.0002759868,0.000185704,0.00003542408],"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":[0.000895309,0.0001757799,0.00186555,0.0002789571,0.0002238256,0.0004410879,0.0002858377,0.1224583,0.2627948,0.09458295,0.003214265,0.5127835],"study_design_scores_gemma":[0.0000227573,0.00009821486,0.00164149,0.00001993547,0.00009566981,0.0006252873,0.00002648527,0.8686009,0.106231,0.01814298,0.004448498,0.00004675844],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01242456,0.0000800184,0.9862334,0.00003536975,0.00003099795,0.00001513232,0.00002374144,0.0002058354,0.0009509862],"genre_scores_gemma":[0.165548,0.0002358056,0.8287123,0.00005181914,0.00004475856,0.00004461141,0.0001659954,0.0002819633,0.004914813],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001966308,"threshold_uncertainty_score":0.006577909,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2156668482","doi":"10.1109/icip.2008.4712109","title":"Efficient nonlocal-means denoising using the SVD","year":2008,"lang":"en","type":"article","venue":"","topic":"Image and Signal Denoising Methods","field":"Computer Science","cited_by":139,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Waterloo","funders":"","keywords":"Pixel; Non-local means; Weighting; Noise reduction; Computation; Computer science; Artificial intelligence; Algorithm; Image denoising; Pattern recognition (psychology); Image (mathematics); Neighbourhood (mathematics); Mathematics; Singular value decomposition; Physics","authors":[{"name":"Jeff Orchard","is_ca":true},{"name":"Mehran Ebrahimi","is_ca":true},{"name":"Alexander Wong","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.05210123361907937,"gpt":0.2930434858694562,"spread":0.2409422522503768,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001192285,0.0008428459,0.00148713,0.001008499,0.0005495703,0.000899978,0.00123577,0.00107902,0.001510603],"category_scores_gemma":[0.002781888,0.0005045881,0.001214327,0.001068776,0.000594095,0.001463042,0.001173747,0.001121663,0.0008129052],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000559685,"about_ca_system_score_gemma":0.0008635753,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002798065,"about_ca_topic_score_gemma":0.004855581,"domain_scores_codex":[0.9991999,0.000173237,0.00004721587,0.000174753,0.0003520747,0.00005288673],"domain_scores_gemma":[0.9991333,0.0003663635,0.00007255364,0.00013989,0.0002565774,0.00003138597],"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":[0.0003144089,0.0001201002,0.0008117982,0.0004039367,0.000208506,0.0001674803,0.0003065018,0.2083818,0.1123262,0.0273222,0.005813081,0.6438239],"study_design_scores_gemma":[0.0000182595,0.00003567996,0.0002714534,0.00001018376,0.00002156414,0.0001094691,0.00002804454,0.965655,0.02172731,0.009084327,0.003019449,0.00001928405],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.003309605,0.0001199954,0.9959397,0.00003994598,0.00002024443,0.00001047013,0.00001291875,0.0001942386,0.0003528704],"genre_scores_gemma":[0.08159632,0.0003733289,0.9148734,0.00006482228,0.00005833885,0.00006965879,0.0001914957,0.0001556782,0.002616871],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002798065,"threshold_uncertainty_score":0.006305516,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W1529631489","doi":"10.1109/icip.2003.1247253","title":"Adaptive Wiener filtering of noisy images and image sequences","year":2004,"lang":"en","type":"article","venue":"","topic":"Image and Signal Denoising Methods","field":"Computer Science","cited_by":138,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Waterloo","funders":"","keywords":"Wiener filter; Wiener deconvolution; Filter (signal processing); Wavelet; Mathematics; Noise reduction; Artificial intelligence; Pattern recognition (psychology); Noise (video); Non-local means; Adaptive filter; Computer science; Image (mathematics); Computer vision; Wavelet transform; Boundary (topology); Algorithm; Image denoising; Deconvolution; Blind deconvolution; Mathematical analysis","authors":[{"name":"Fubao Jin","is_ca":true},{"name":"Paul Fieguth","is_ca":true},{"name":"L.L. Winger","is_ca":true},{"name":"E. Jernigan","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.02234193907923259,"gpt":0.2728359753018199,"spread":0.2504940362225873,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009802511,0.0007170365,0.0008020056,0.0004952118,0.0002122386,0.000651231,0.0005769212,0.0009893464,0.0007657238],"category_scores_gemma":[0.002546665,0.0002743995,0.0006825497,0.0005055594,0.0006693926,0.001279637,0.0004954344,0.0005155339,0.0003663716],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002485287,"about_ca_system_score_gemma":0.0003087708,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00103666,"about_ca_topic_score_gemma":0.00114273,"domain_scores_codex":[0.9995522,0.0001092063,0.00002897361,0.00009129398,0.0001848142,0.00003352131],"domain_scores_gemma":[0.9996131,0.0002030716,0.00004599195,0.00003618674,0.00008872674,0.00001293725],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0004175343,0.0001056904,0.001476733,0.0006274109,0.0002238512,0.0006576769,0.0002742478,0.4002113,0.2573896,0.07591554,0.001042356,0.261658],"study_design_scores_gemma":[0.000009508249,0.0001511567,0.000846958,0.00002147877,0.00004235998,0.0002456858,0.00003402886,0.9422824,0.03838412,0.01481444,0.003139397,0.00002854983],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01926106,0.0005067535,0.979212,0.00005379759,0.00005677078,0.00001704613,0.00001214271,0.00009237674,0.0007880629],"genre_scores_gemma":[0.3091747,0.002107644,0.6803013,0.0001409899,0.0002301655,0.00009519932,0.0001400164,0.00008799772,0.007722038],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.00103666,"threshold_uncertainty_score":0.005184174,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2129967410","doi":"10.1109/tbme.2009.2028876","title":"Wavelet-Domain Medical Image Denoising Using Bivariate Laplacian Mixture Model","year":2009,"lang":"en","type":"article","venue":"IEEE Transactions on Biomedical Engineering","topic":"Image and Signal Denoising Methods","field":"Computer Science","cited_by":132,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Queen's University","funders":"","keywords":"Noise reduction; Wavelet; Artificial intelligence; Pattern recognition (psychology); Noise (video); Wavelet transform; Bivariate analysis; Mathematics; Probability density function; Algorithm; Computer science; Image (mathematics); Statistics","authors":[{"name":"Hossein Rabbani","is_ca":false},{"name":"Reza Nezafat","is_ca":false},{"name":"Saeed Gazor","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.0132928529449564,"gpt":0.2613736860493035,"spread":0.2480808331043471,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001077867,0.0005965838,0.0008549792,0.0008694585,0.0001706019,0.0006287604,0.0009452862,0.0007806999,0.0006206973],"category_scores_gemma":[0.001843051,0.0003803718,0.001435997,0.0009196338,0.0004808357,0.00112074,0.0007454118,0.0009707661,0.0004101969],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004038827,"about_ca_system_score_gemma":0.0004450988,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00142077,"about_ca_topic_score_gemma":0.001225958,"domain_scores_codex":[0.9995434,0.0001066463,0.00002493639,0.00008586856,0.0002078246,0.00003142108],"domain_scores_gemma":[0.9995561,0.0001771393,0.00005225442,0.0000590219,0.0001367667,0.00001859284],"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":[0.0001836333,0.00007680486,0.001208972,0.0002602399,0.0001961139,0.0002335464,0.0001703927,0.6871337,0.06220326,0.04074692,0.002009416,0.2055771],"study_design_scores_gemma":[0.000003358107,0.0000163958,0.0001449055,0.000004302127,0.00001336158,0.00005401892,0.000004314536,0.993222,0.002891782,0.002879688,0.0007572596,0.00000855727],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.003662044,0.0001575145,0.9957328,0.00006234506,0.00001098991,0.000007232736,0.00001323256,0.0001055456,0.0002483341],"genre_scores_gemma":[0.3469921,0.002436534,0.6446502,0.0002475534,0.0001498329,0.0001171213,0.0004554107,0.0002082947,0.004742908],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.00142077,"threshold_uncertainty_score":0.00570035,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2900613774","doi":"10.1109/tc.2018.2880742","title":"Efficient Mitchell’s Approximate Log Multipliers for Convolutional Neural Networks","year":2018,"lang":"en","type":"article","venue":"IEEE Transactions on Computers","topic":"Image and Signal Denoising Methods","field":"Computer Science","cited_by":130,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"","funders":"Erzincan Üniversitesi; University of Toronto","keywords":"Convolutional neural network; Computer science; Algorithm; Parallel computing; Arithmetic; Artificial intelligence; Mathematics","authors":[{"name":"Min Soo Kim","is_ca":false},{"name":"Alberto A. Del Barrio","is_ca":false},{"name":"Leonardo Tavares Oliveira","is_ca":false},{"name":"R. Hermida","is_ca":false},{"name":"Nader Bagherzadeh","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.0253079684981974,"gpt":0.2708273379042991,"spread":0.2455193694061017,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004306663,0.0007997748,0.000407347,0.0005643783,0.0003874071,0.0008869426,0.001315466,0.0004373467,0.004167704],"category_scores_gemma":[0.002049202,0.0003089062,0.0003104998,0.0005618671,0.0004387987,0.001998954,0.0007882584,0.0008472747,0.0009065653],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006441533,"about_ca_system_score_gemma":0.0009064202,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001234801,"about_ca_topic_score_gemma":0.004193637,"domain_scores_codex":[0.9996505,0.0000698389,0.0000299942,0.00006024637,0.0001469972,0.00004239435],"domain_scores_gemma":[0.9996213,0.0001400891,0.00004763232,0.0000878167,0.00008770882,0.00001538673],"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":[0.0005622496,0.0001425092,0.001075426,0.0002932817,0.00006687046,0.0003034634,0.0001460014,0.1498849,0.06491503,0.09102756,0.008868449,0.6827143],"study_design_scores_gemma":[0.00005657852,0.0004019908,0.0003696043,0.00005948408,0.00004180421,0.0003865581,0.00006152409,0.8892009,0.05088615,0.03953429,0.01896089,0.00004023118],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.03734715,0.0009477268,0.9544476,0.0001990118,0.0001277974,0.00006782118,0.00009091659,0.001404295,0.005367666],"genre_scores_gemma":[0.4091834,0.0006008327,0.5810432,0.0002015161,0.00008794054,0.000152483,0.0002476775,0.0001779853,0.008304972],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.004167704,"threshold_uncertainty_score":0.01394236,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2138448681","doi":"","title":"Learning Sparse Topographic Representations with Products of Student-t Distributions","year":2002,"lang":"en","type":"article","venue":"Neural Information Processing Systems","topic":"Image and Signal Denoising Methods","field":"Computer Science","cited_by":129,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Toronto","funders":"","keywords":"Iterated function; Computer science; Filter (signal processing); Artificial intelligence; Pattern recognition (psychology); Product (mathematics); Wiener filter; Orientation (vector space); Algorithm; Computer vision; Mathematics","authors":[{"name":"Max Welling","is_ca":true},{"name":"Simon Osindero","is_ca":false},{"name":"Geoffrey E. Hinton","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.03812047916899464,"gpt":0.2898028860270126,"spread":0.251682406858018,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001582332,0.0004057836,0.0006692153,0.0006286665,0.0002888118,0.001101724,0.001233391,0.001198232,0.001714361],"category_scores_gemma":[0.008597187,0.0005655424,0.0008276283,0.0008157025,0.001049635,0.003079217,0.00108659,0.00143431,0.0004719165],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005058588,"about_ca_system_score_gemma":0.0005729011,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001195596,"about_ca_topic_score_gemma":0.001517717,"domain_scores_codex":[0.9994767,0.0001894616,0.00002533658,0.0001306288,0.0001334038,0.0000443834],"domain_scores_gemma":[0.99749,0.001407193,0.0003502537,0.000337477,0.0003239878,0.00009094469],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0001421652,0.00006371064,0.0017574,0.00008265562,0.00007154243,0.000170869,0.0002077974,0.7723244,0.005136193,0.1221327,0.00141328,0.09649727],"study_design_scores_gemma":[0.000005621746,0.00001655885,0.0000892597,0.00000217759,0.000003265927,0.00003293253,0.000005722291,0.9634411,0.0006212732,0.03558001,0.0001965296,0.000005569747],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01638658,0.00003797245,0.9829385,0.00009733099,0.000006955902,0.00001214035,0.00002267341,0.0001315203,0.000366346],"genre_scores_gemma":[0.6457516,0.000353672,0.3493744,0.0001919906,0.00008559967,0.0001658119,0.0002476991,0.0001288192,0.00370037],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001714361,"threshold_uncertainty_score":0.008368313,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2002903570","doi":"10.1007/s10278-008-9138-8","title":"Image Texture Characterization Using the Discrete Orthonormal S-Transform","year":2008,"lang":"en","type":"review","venue":"Journal of Digital Imaging","topic":"Image and Signal Denoising Methods","field":"Computer Science","cited_by":122,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Alberta Cancer Foundation; University of Calgary","funders":"","keywords":"Orthonormal basis; Wavelet; Artificial intelligence; Spatial frequency; Computer science; Texture filtering; Wavelet transform; Pattern recognition (psychology); Discrete wavelet transform; Frequency domain; Image texture; Texture compression; Texture (cosmology); Computer vision; Fourier transform; Invariant (physics); Image (mathematics); Mathematics; Image processing; Optics; Mathematical analysis","authors":[{"name":"Sylvia Drabycz","is_ca":true},{"name":"R. G. Stockwell","is_ca":false},{"name":"J. Ross Mitchell","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.03358968378570713,"gpt":0.325932053720819,"spread":0.2923423699351119,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003595189,0.0004805837,0.0006769555,0.002196625,0.0001600904,0.0007934531,0.0004919459,0.0005218333,0.001415669],"category_scores_gemma":[0.0007583694,0.000206741,0.0004217704,0.00155928,0.0007965486,0.0008622618,0.0003136245,0.0005413198,0.0008651983],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002943349,"about_ca_system_score_gemma":0.0002577974,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006464657,"about_ca_topic_score_gemma":0.000612137,"domain_scores_codex":[0.9997352,0.0000374921,0.00001527835,0.00005018198,0.0001462776,0.00001570025],"domain_scores_gemma":[0.999697,0.00008480546,0.00004606539,0.00004030044,0.0001185202,0.00001323967],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00007947356,0.00003917091,0.00116891,0.0008336796,0.00005501944,0.0002102829,0.00009534926,0.01838927,0.1457919,0.03945059,0.003890903,0.7899956],"study_design_scores_gemma":[0.0000351682,0.0002932163,0.005896336,0.000186994,0.0001137521,0.002288486,0.0002171169,0.6462774,0.2117738,0.04737319,0.08539691,0.0001475995],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"review","genre_scores_codex":[0.01693101,0.008828175,0.9667054,0.0002738456,0.0001427571,0.00004400042,0.0001243302,0.0004256088,0.006524884],"genre_scores_gemma":[0.273928,0.0218878,0.6931788,0.0001925935,0.0004837684,0.0001127864,0.0004371079,0.0001687451,0.00961052],"genre_candidate":"review","genre_consensus":null,"teacher_disagreement_score":0.002196625,"threshold_uncertainty_score":0.004735887,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2111261932","doi":"10.1007/s003659910010","title":"Biorthogonal Multiwavelets on the Interval: Cubic Hermite Splines","year":2000,"lang":"en","type":"article","venue":"Constructive Approximation","topic":"Image and Signal Denoising Methods","field":"Computer Science","cited_by":117,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Alberta","funders":"","keywords":"Mathematics; Biorthogonal system; Hermite polynomials; Wavelet; Interval (graph theory); Applied mathematics; Basis (linear algebra); Multiresolution analysis; Hermite interpolation; Sobolev space; Moment (physics); Sequence (biology); Mathematical analysis; Pure mathematics; Combinatorics; Wavelet transform; Geometry","authors":[{"name":"Wolfgang Dahmen","is_ca":false},{"name":"Bin Han","is_ca":false},{"name":"Rong Qing Jia","is_ca":true},{"name":"Angela Kunoth","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.02499734994413985,"gpt":0.2674536643865762,"spread":0.2424563144424363,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001085953,0.0004638545,0.0005867844,0.0009308529,0.000259333,0.001342143,0.000718537,0.0009852209,0.001968908],"category_scores_gemma":[0.003485406,0.0003460267,0.0004326783,0.001259848,0.001078934,0.001362928,0.0007998732,0.002480577,0.0009129643],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003594447,"about_ca_system_score_gemma":0.000422641,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006873302,"about_ca_topic_score_gemma":0.0006470202,"domain_scores_codex":[0.9995416,0.0001563739,0.0000168634,0.00005955389,0.0001894208,0.00003608421],"domain_scores_gemma":[0.998911,0.0005098623,0.0001089488,0.0001641699,0.0002289818,0.00007697534],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.000254774,0.00009561477,0.0005052804,0.0002530469,0.00003085239,0.00013793,0.0002683226,0.1709395,0.02154293,0.5330887,0.004548663,0.2683343],"study_design_scores_gemma":[0.00001160126,0.00003303111,0.0001942452,0.00002754459,0.000009799548,0.0001139901,0.00003934902,0.8944065,0.003474092,0.09517169,0.00649541,0.00002281453],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.006681245,0.0003348887,0.9910931,0.0001014878,0.00006472294,0.00000633515,0.00002419304,0.00009064464,0.001603375],"genre_scores_gemma":[0.3215367,0.002631871,0.6629696,0.0001264026,0.0003434851,0.00005955551,0.0001895763,0.0002789622,0.01186382],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.001968908,"threshold_uncertainty_score":0.006586671,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2948837518","doi":"10.1137/18m1230451","title":"Color Image Restoration by Saturation-Value Total Variation","year":2019,"lang":"en","type":"article","venue":"SIAM Journal on Imaging Sciences","topic":"Image and Signal Denoising Methods","field":"Computer Science","cited_by":113,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Toronto Metropolitan University","funders":"National Natural Science Foundation of China; Hong Kong Baptist University; Natural Science Foundation of Shanghai","keywords":"Hue; Color image; Artificial intelligence; Color space; Mathematics; Color balance; Computer vision; Total variation denoising; Image restoration; Image gradient; RGB color model; Pattern recognition (psychology); Color histogram; Regularization (linguistics); Computer science; Image processing; Image (mathematics)","authors":[{"name":"Zhigang Jia","is_ca":false},{"name":"Michael K. Ng","is_ca":true},{"name":"Wei Wang","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.00952820053329607,"gpt":0.2837713081217957,"spread":0.2742431075884997,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000791857,0.0006625058,0.0005114326,0.0006385871,0.0002536301,0.0006343258,0.0006790902,0.0005245188,0.001016488],"category_scores_gemma":[0.001333742,0.0002420615,0.0008983193,0.0006342518,0.0008521366,0.0008238739,0.0009264735,0.0007270052,0.0002482834],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005146771,"about_ca_system_score_gemma":0.0005857724,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001478085,"about_ca_topic_score_gemma":0.001252201,"domain_scores_codex":[0.9996002,0.0001075273,0.000015132,0.00006602462,0.0001813174,0.00002973415],"domain_scores_gemma":[0.9996762,0.0001169973,0.00004469796,0.00004705919,0.00009782503,0.0000172121],"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":[0.0002457979,0.00008027812,0.0009078066,0.0003668311,0.0001375095,0.0001859879,0.0002886927,0.4678699,0.1370803,0.1066628,0.003518707,0.2826554],"study_design_scores_gemma":[0.000007439121,0.00004719594,0.00017944,0.000008777941,0.00001439735,0.00009876324,0.00001297732,0.9739234,0.01380218,0.009226066,0.002662017,0.00001739875],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.008726764,0.0002252448,0.9894273,0.00007138493,0.00002280251,0.00001475767,0.00001082399,0.0001496643,0.001351271],"genre_scores_gemma":[0.5080624,0.001068885,0.48171,0.0001906643,0.00009624581,0.00009394842,0.000122835,0.0003295572,0.00832554],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.001478085,"threshold_uncertainty_score":0.004187822,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2129623024","doi":"10.1109/icassp.2004.1326408","title":"Image denoising using neighbouring wavelet coefficients","year":2004,"lang":"en","type":"article","venue":"","topic":"Image and Signal Denoising Methods","field":"Computer Science","cited_by":112,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Concordia University","funders":"","keywords":"Wavelet; Non-local means; Discrete wavelet transform; Second-generation wavelet transform; Wavelet transform; Stationary wavelet transform; Artificial intelligence; Lifting scheme; Wavelet packet decomposition; Pattern recognition (psychology); Thresholding; Mathematics; Noise reduction; Cascade algorithm; Computer science; Image (mathematics); Image denoising","authors":[{"name":"G.Y. Chen","is_ca":true},{"name":"Tien D. Bui","is_ca":true},{"name":"Adam Krzyżak","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.02893082178170158,"gpt":0.2948311578804105,"spread":0.2659003360987089,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000772766,0.0003635511,0.000949176,0.000682022,0.0002940704,0.0005472119,0.000588654,0.0007922816,0.0008243129],"category_scores_gemma":[0.002378182,0.0002734408,0.0007445315,0.0006800123,0.0006708883,0.0009856635,0.0006056743,0.0005958845,0.0003560158],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002036432,"about_ca_system_score_gemma":0.0002598198,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004954075,"about_ca_topic_score_gemma":0.0008383244,"domain_scores_codex":[0.9996885,0.0000564895,0.00002391197,0.0000843759,0.0001206801,0.00002600834],"domain_scores_gemma":[0.9992938,0.0002694748,0.0000785141,0.0001491692,0.0001680914,0.00004099458],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000592878,0.0001010921,0.002136898,0.0004000595,0.0001387501,0.0002995296,0.0003392381,0.04864657,0.4843481,0.01442201,0.001015983,0.4475589],"study_design_scores_gemma":[0.00005976328,0.0004821555,0.005969652,0.00006616712,0.0002078239,0.001069192,0.0001242597,0.6741524,0.2974923,0.01178701,0.008509593,0.00007959607],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.145341,0.001086897,0.8508127,0.0001119968,0.0001070471,0.00003447775,0.00003179545,0.0004326538,0.002041432],"genre_scores_gemma":[0.5437611,0.001156391,0.452217,0.0001052033,0.00005785094,0.00004045165,0.000107626,0.0001596579,0.002394818],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.000949176,"threshold_uncertainty_score":0.004086792,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2148877501","doi":"10.1016/j.acha.2004.05.003","title":"Symmetric wavelet tight frames with two generators","year":2004,"lang":"en","type":"article","venue":"Applied and Computational Harmonic Analysis","topic":"Image and Signal Denoising Methods","field":"Computer Science","cited_by":107,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"","funders":"University of Alberta","keywords":"Wavelet; Mathematics; Antisymmetric relation; Filter (signal processing); Matching (statistics); Complement (music); Gabor wavelet; Algorithm; Scaling; Simple (philosophy); Wavelet transform; Pure mathematics; Discrete wavelet transform; Computer science; Artificial intelligence; Geometry; Statistics; Computer vision","authors":[{"name":"Ivan Selesnick","is_ca":false},{"name":"A. Farras Abdelnour","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.01053311778629306,"gpt":0.2445633810945828,"spread":0.2340302633082897,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001346143,0.001132994,0.0007919231,0.001523331,0.0007342597,0.001831527,0.0008015373,0.001598867,0.007963837],"category_scores_gemma":[0.002769249,0.0004678769,0.0006069277,0.001041563,0.001921763,0.002322004,0.002677877,0.001925154,0.002045288],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005096183,"about_ca_system_score_gemma":0.0004761282,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002456947,"about_ca_topic_score_gemma":0.0002533928,"domain_scores_codex":[0.9992486,0.0002329703,0.0000397582,0.0002046386,0.0001789005,0.00009505361],"domain_scores_gemma":[0.9991697,0.0001437468,0.0001294524,0.0001866379,0.0002065202,0.0001638978],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0001367292,0.00002855879,0.0001131478,0.00004724544,0.00001332713,0.0001401206,0.000160406,0.002639716,0.010131,0.9482369,0.001586212,0.03676656],"study_design_scores_gemma":[0.00005847579,0.00025849,0.00050645,0.00003772936,0.00003167054,0.0004319219,0.0002560473,0.07397895,0.009706252,0.8981888,0.01648711,0.00005821816],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03155079,0.0003787953,0.95718,0.0003726217,0.0004443841,0.00004469812,0.00008510281,0.0001807335,0.009762816],"genre_scores_gemma":[0.6451806,0.0009865832,0.3125155,0.0005470565,0.0009461195,0.0001734661,0.0003256091,0.0005754625,0.03874954],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007963837,"threshold_uncertainty_score":0.02664167,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null}]}