3D AVO Crossplotting — an effective visualization technique
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
PreviousNext No AccessSEG Technical Program Expanded Abstracts 20033D AVO Crossplotting — an effective visualization techniqueAuthors: Satinder ChopraVladimir AlexeevYong XuSatinder ChopraCore Laboratories Reservoir Technologies Division, Calgary, Vladimir AlexeevCore Laboratories Reservoir Technologies Division, Calgary, and Yong XuCore Laboratories Reservoir Technologies Division, Calgaryhttps://doi.org/10.1190/1.1817690 SectionsAboutPDF/ePub ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinked InReddit Permalink: https://doi.org/10.1190/1.1817690FiguresReferencesRelatedDetailsCited byAVO forward modeling and attributes analysis for fluid's identification: a case study7 January 2015 | Acta Geodaetica et Geophysica, Vol. 50, No. 4 SEG Technical Program Expanded Abstracts 2003 ISSN (print):1052-3812 ISSN (online):1949-4645 Copyright: 2003 Pages: 2452 publication data© 2003 Copyright © 2003 Society of Exploration GeophysicistsPublisher:Society of Exploration Geophysicists HistoryPublished Online: 03 Jan 2005 CITATION INFORMATION Satinder Chopra, Vladimir Alexeev, and Yong Xu, (2003), "3D AVO Crossplotting — an effective visualization technique," SEG Technical Program Expanded Abstracts : 189-192. https://doi.org/10.1190/1.1817690 Plain-Language Summary PDF DownloadLoading ...
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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 | 0.007 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.001 | 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 it