Bibliographic record
Abstract
PreviousNext No AccessSEG Technical Program Expanded Abstracts 20022D and 3D anisotropic depth migration case historiesAuthors: Rob VestrumRob VestrumVeritas GeoServices Ltd.https://doi.org/10.1190/1.1816881 SectionsAboutPDF/ePub ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinked InRedditEmail Permalink: https://doi.org/10.1190/1.1816881FiguresReferencesRelatedDetailsCited byDepth migration as a valuable tool for prospect derisking in the Colombian FoothillsSergio Ibanez Poveda, Mario Patino, and John Mathewson5 July 2017 | Interpretation, Vol. 5, No. 4Reflection point sideslip and smear in imaging below dipping anisotropic media1 December 2009 | Geophysical Prospecting, Vol. 58, No. 4The effects of anisotropy on Canadian foothills exploration: A case historyMargaret A. Stratton and Peter Vermeulen6 October 2006Overcoming thrust‐belt imaging problems in Magdalena Valley, ColombiaJon Gittins, Rob Vestrum, and Ralph Gillcrist3 January 2005Sideslip and smear beneath dipping transversely isotropic strataRob Vestrum and Peter Vermeulen3 January 20053D anisotropic depth migration in Blackstone, Alberta: A case historyRob Vestrum, Dave Mackie, and Alana Schoepp3 January 2005Impact of the effects of anisotropy in Canadian foothills exploration: A case historyMargaret A. Stratton3 January 2005 SEG Technical Program Expanded Abstracts 2002ISSN (print):1052-3812 ISSN (online):1949-4645Copyright: 2002 Pages: 2478 publication data© 2002 Copyright © 2002 Society of Exploration GeophysicistsPublisher:Society of Exploration Geophysicists HistoryPublished Online: 03 Jan 2005 CITATION INFORMATION Rob Vestrum, (2002), "2D and 3D anisotropic depth migration case histories," SEG Technical Program Expanded Abstracts : 1256-1259. https://doi.org/10.1190/1.1816881 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".