Investigations into Inversion of Magnetic and Gradient Magnetic Data for Detection and Discrimination of Metallic Objects
Bibliographic record
Abstract
PreviousNext No AccessSymposium on the Application of Geophysics to Engineering and Environmental Problems 2003Investigations into Inversion of Magnetic and Gradient Magnetic Data for Detection and Discrimination of Metallic ObjectsAuthors: R. W. GroomRuizhong JiaCatalina AlvarezR. W. GroomPetRos EiKon, Concord, Ontario, Canada, Ruizhong JiaPetRos EiKon, Concord, Ontario, Canada, and Catalina AlvarezPetRos EiKon, Concord, Ontario, Canadahttps://doi.org/10.4133/1.2923147 SectionsAboutPDF/ePub ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinked InRedditEmail Abstract Introductory paragraph for this paper is available only in the PDF and GZipped PS filesPermalink: https://doi.org/10.4133/1.2923147FiguresReferencesRelatedDetailsCited ByCollection and Analysis of 3D Magnetic Data for UXO DiscriminationT. Jeffrey Gamey21 June 2012 | Journal of Environmental and Engineering Geophysics, Vol. 11, No. 3 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: 30 Sep 2008 CITATION INFORMATION R. W. Groom, Ruizhong Jia, and Catalina Alvarez, (2003), "Investigations into Inversion of Magnetic and Gradient Magnetic Data for Detection and Discrimination of Metallic Objects," Symposium on the Application of Geophysics to Engineering and Environmental Problems Proceedings : 1406-1413. https://doi.org/10.4133/1.2923147 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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".