Multi‐offset VSP traveltime inversion for anisotropy analysis
Bibliographic record
Abstract
PreviousNext No AccessSEG Technical Program Expanded Abstracts 2000Multi‐offset VSP traveltime inversion for anisotropy analysisAuthors: Rachel T. NewrickDon C. LawtonDeborah A. SprattRachel T. NewrickThe Department of Geology and Geophysics, University of Calgary, AB T2N 1N4, Canada, Don C. LawtonThe Department of Geology and Geophysics, University of Calgary, AB T2N 1N4, Canada, and Deborah A. SprattThe Department of Geology and Geophysics, University of Calgary, AB T2N 1N4, Canadahttps://doi.org/10.1190/1.1815900 SectionsAboutPDF/ePub ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinked InReddit Permalink: https://doi.org/10.1190/1.1815900FiguresReferencesRelatedDetailsCited byThe method and application of anisotropic velocity model building using trend velocity constraintsRen Ting, Li Xiaozhang, He Jianwei, Deng Yong, and Qin Dianming11 December 2018Estimation of anisotropy parameters in VTI media from surface P‐wave seismic data and VSP in Tarim Basin, ChinaXiaoming Luo, Yun Lin, Lin Wu, and Fengchang Yao7 December 2005 SEG Technical Program Expanded Abstracts 2000ISSN (print):1052-3812 ISSN (online):1949-4645Copyright: 2000 Pages: 2484 publication data© 2000 Copyright © 2000 Society of Exploration GeophysicistsPublisher:Society of Exploration Geophysicists HistoryPublished Online: 04 Jan 2005 CITATION INFORMATION Rachel T. Newrick, Don C. Lawton, and Deborah A. Spratt, (2000), "Multi‐offset VSP traveltime inversion for anisotropy analysis," SEG Technical Program Expanded Abstracts : 2237-2240. https://doi.org/10.1190/1.1815900 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 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| 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) | 0.013 | 0.003 |
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".