Multiple suppression based on the migration operator and a hyperbolic median filter
Bibliographic record
Abstract
PreviousNext No AccessSEG Technical Program Expanded Abstracts 2003Multiple suppression based on the migration operator and a hyperbolic median filterAuthors: Rongfeng ZhangTadeusz J. UlrychRongfeng ZhangUniversity of British Columbia, Canada and Tadeusz J. UlrychUniversity of British Columbia, Canadahttps://doi.org/10.1190/1.1817705 SectionsAboutPDF/ePub ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinked InRedditEmail Permalink: https://doi.org/10.1190/1.1817705FiguresReferencesRelatedDetailsCited ByMultiple suppression based on polynomial fittingZou Zhen, Ling Yun, Wang Yong Jun, and Gao Jun25 October 2012Simultaneous sources separation via multidirectional vector-median filteringShoudong Huo, Yi Luo, and Panos G. Kelamis14 June 2012 | GEOPHYSICS, Vol. 77, No. 4Simultaneous sources separation via multi‐directional vector‐median filterShoudong Huo, Yi Luo, and Panos Kelamis14 October 2009A 1D time-varying median filter for seismic random, spike-like noise eliminationGEOPHYSICS, Vol. 74, No. 1A 2D multistage median filter to reduce random seismic noiseGEOPHYSICS, Vol. 71, No. 5 SEG Technical Program Expanded Abstracts 2003ISSN (print):1052-3812 ISSN (online):1949-4645Copyright: 2003 Pages: 2452 publication data© 2003 Copyright © 2003 Society of Exploration GeophysicistsPublisher:Society of Exploration Geophysicists HistoryPublished: 03 Jan 2005 CITATION INFORMATION Rongfeng Zhang and Tadeusz J. Ulrych, (2003), "Multiple suppression based on the migration operator and a hyperbolic median filter," SEG Technical Program Expanded Abstracts : 1949-1952. https://doi.org/10.1190/1.1817705 Plain-Language Summary PDF DownloadLoading ...
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".