Inversion of 3D Electromagnetic Data ‐ A constrained optimization approach
Bibliographic record
Abstract
PreviousNext No AccessSEG Technical Program Expanded Abstracts 2000Inversion of 3D Electromagnetic Data ‐ A constrained optimization approachAuthors: E. HaberD. W. OldenburgU. AscherE. HaberUBC‐Geophysical Inversion Facility, UBC, Vancouver, D. W. OldenburgUBC‐Geophysical Inversion Facility, UBC, Vancouver, and U. AscherUBC‐Geophysical Inversion Facility, UBC, Vancouverhttps://doi.org/10.1190/1.1816051 SectionsAboutPDF/ePub ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinked InRedditEmail Permalink: https://doi.org/10.1190/1.1816051FiguresReferencesRelatedDetailsCited byParallel inversion of large-scale airborne time-domain electromagnetic data with multiple OcTree meshes1 May 2014 | Inverse Problems, Vol. 30, No. 5A parallel method for large scale time domain electromagnetic inverse problemsApplied Numerical Mathematics, Vol. 58, No. 4 SEG Technical Program Expanded Abstracts 2000ISSN (print):1052-3812 ISSN (online):1949-4645Copyright: 2000 Pages: 2484 publication data© 2000 Copyright © 2000 Society of Exploration GeophysicistsPublisher:Society of Exploration Geophysicists HistoryPublished Online: 04 Jan 2005 CITATION INFORMATION E. Haber, D. W. Oldenburg, and U. Ascher, (2000), "Inversion of 3D Electromagnetic Data ‐ A constrained optimization approach," SEG Technical Program Expanded Abstracts : 304-307. https://doi.org/10.1190/1.1816051 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 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) | 0.017 | 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".