Regularized focusing inversion of 3‐D gravity tensor data
Bibliographic record
Abstract
PreviousNext No AccessSEG Technical Program Expanded Abstracts 2002Regularized focusing inversion of 3‐D gravity tensor dataAuthors: Michael S. ZhdanovRobert G. EllisSouvik MukherjeeDmitriy PavlovMichael S. ZhdanovUniversity of Utah, Department of Geology and Geophysics, Salt Lake City, UT 84112, Robert G. EllisBHP Billiton World Exploration Inc. 1111 West Georgia Street, Suite 1400, Vancouver, BC, Canada, V6E 4M3, Souvik MukherjeeUniversity of Utah, Department of Geology and Geophysics, Salt Lake City, UT 84112, and Dmitriy PavlovUniversity of Utah, Department of Geology and Geophysics, Salt Lake City, UT 84112https://doi.org/10.1190/1.1817366 SectionsAboutPDF/ePub ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinked InRedditEmail Permalink: https://doi.org/10.1190/1.1817366FiguresReferencesRelatedDetailsCited ByThree‐dimensional regularized focusing inversion of gravity gradient tensor component dataMichael S. Zhdanov, Robert Ellis, and Souvik Mukherjee28 July 2004 | GEOPHYSICS, Vol. 69, No. 4 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 Michael S. Zhdanov, Robert G. Ellis, Souvik Mukherjee, and Dmitriy Pavlov, (2002), "Regularized focusing inversion of 3‐D gravity tensor data," SEG Technical Program Expanded Abstracts : 751-754. https://doi.org/10.1190/1.1817366 Plain-Language Summary PDF DownloadLoading ...
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.004 | 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 teacher head, 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".