3D AVO Crossplotting — an effective visualization technique
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 ...
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.035 | 0.008 |
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".