Discrimination and Classification of UXO Using Magnetometry: Inversion and Error Analysis Using Robust Statistics
Bibliographic record
Abstract
Previous No AccessSymposium on the Application of Geophysics to Engineering and Environmental Problems 2003Discrimination and Classification of UXO Using Magnetometry: Inversion and Error Analysis Using Robust StatisticsAuthors: Stephen D. BillingsLeonard R. PasionDouglas W. OldenburgStephen D. BillingsUBC‐Geophysical Inversion Facility, Vancouver, BC, Leonard R. PasionUBC‐Geophysical Inversion Facility, Vancouver, BC, and Douglas W. OldenburgUBC‐Geophysical Inversion Facility, Vancouver, BChttps://doi.org/10.4133/1.2923155 SectionsAboutPDF/ePub ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinked InReddit Abstract Introductory paragraph for this paper is available only in the PDF and GZipped PS filesPermalink: https://doi.org/10.4133/1.2923155FiguresReferencesRelatedDetails Symposium on the Application of Geophysics to Engineering and Environmental Problems 2003ISSN (online):1554-8015Copyright: 2003 Pages: 1491 publication data© 2003 Copyright © 2003 The Environmental and Engineering Geophysical SocietyPublisher:Environmental & Engineering Geophysical Society HistoryPublished Online: 30 Sep 2008 CITATION INFORMATION Stephen D. Billings, Leonard R. Pasion, and Douglas W. Oldenburg, (2003), "Discrimination and Classification of UXO Using Magnetometry: Inversion and Error Analysis Using Robust Statistics," Symposium on the Application of Geophysics to Engineering and Environmental Problems Proceedings : 1479-1491. https://doi.org/10.4133/1.2923155 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 | 0.001 |
| 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) | 0.000 | 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".