Fast 3‐D inversion of multi‐source array electromagnetic data collected for mineral exploration
Bibliographic record
Abstract
PreviousNext No AccessSEG Technical Program Expanded Abstracts 2001Fast 3‐D inversion of multi‐source array electromagnetic data collected for mineral explorationAuthors: Efthimios TartarasMichael ZhdanovStephen BalchEfthimios TartarasDepartment of Geology and Geophysics, University of Utah, Salt Lake City, UT 84112, Michael ZhdanovDepartment of Geology and Geophysics, University of Utah, Salt Lake City, UT 84112, and Stephen BalchINCO Technical Services Limited, Copper Cliff, Ontario, Canada P0M 1N0https://doi.org/10.1190/1.1816374 SectionsAboutPDF/ePub ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinked InRedditEmail Permalink: https://doi.org/10.1190/1.1816374FiguresReferencesRelatedDetails SEG Technical Program Expanded Abstracts 2001ISSN (print):1052-3812 ISSN (online):1949-4645Copyright: 2001 Pages: 2135 publication data© 2001 Copyright © 2001 Society of Exploration GeophysicistsPublisher:Society of Exploration Geophysicists HistoryPublished: 03 Jan 2005 CITATION INFORMATION Efthimios Tartaras, Michael Zhdanov, and Stephen Balch, (2001), "Fast 3‐D inversion of multi‐source array electromagnetic data collected for mineral exploration," SEG Technical Program Expanded Abstracts : 1439-1442. https://doi.org/10.1190/1.1816374 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 | 0.001 |
| 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 | 0.000 |
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".