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 machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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 source (direct Gemma or distilled Codex), 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".