Vp/Vs from multicomponent seismic data and automatic PS to PP time mapping
Bibliographic record
Abstract
PreviousNext No AccessSEG Technical Program Expanded Abstracts 2003Vp/Vs from multicomponent seismic data and automatic PS to PP time mappingAuthors: Osareni C. OgiesobaRobert R. StewartOsareni C. OgiesobaGeology and Geophysics, University of Calgary and Robert R. StewartGeology and Geophysics, University of Calgaryhttps://doi.org/10.1190/1.1818054 SectionsAboutPDF/ePub ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinked InRedditEmail Permalink: https://doi.org/10.1190/1.1818054FiguresReferencesRelatedDetailsCited ByAutomated time-domain transform of converted waves by prestack double-parameter scanning9 July 2013 | Journal of Geophysics and Engineering, Vol. 10, No. 4Analysis of converted‐wave extended images for migration velocity analysisJia Yan and Paul Sava21 October 2010 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 Osareni C. Ogiesoba and Robert R. Stewart, (2003), "Vp/Vs from multicomponent seismic data and automatic PS to PP time mapping," SEG Technical Program Expanded Abstracts : 789-792. https://doi.org/10.1190/1.1818054 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.031 | 0.010 |
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".