Extracting meaningful geologic parameters using multiple attribute analysis on AVO derived Lamé rock parameter inversions: 3D seismic case study from southern Alberta, Canada
Bibliographic record
Abstract
PreviousNext No AccessSEG Technical Program Expanded Abstracts 2002Extracting meaningful geologic parameters using multiple attribute analysis on AVO derived Lamé rock parameter inversions: 3D seismic case study from southern Alberta, CanadaAuthors: Douglas PrudenDouglas PrudenGEDCOhttps://doi.org/10.1190/1.1817310 SectionsAboutPDF/ePub ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinked InRedditEmail Permalink: https://doi.org/10.1190/1.1817310FiguresReferencesRelatedDetailsCited ByLatest developments in seismic texture analysis for subsurface structure, facies, and reservoir characterization: A reviewDengliang Gao17 March 2011 | GEOPHYSICS, Vol. 76, No. 2 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: 03 Jan 2005 CITATION INFORMATION Douglas Pruden, (2002), "Extracting meaningful geologic parameters using multiple attribute analysis on AVO derived Lamé rock parameter inversions: 3D seismic case study from southern Alberta, Canada," SEG Technical Program Expanded Abstracts : 560-563. https://doi.org/10.1190/1.1817310 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".