Identification of Electrical Anisotropy from Helicopter EM Data
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
PreviousNext No AccessSymposium on the Application of Geophysics to Engineering and Environmental Problems 2003Identification of Electrical Anisotropy from Helicopter EM DataAuthors: Changchun YinGreg HodgesChangchun YinFugro Airborne Surveys, Mississauga, ON, Canada and Greg HodgesFugro Airborne Surveys, Mississauga, ON, Canadahttps://doi.org/10.4133/1.2923185 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/10.4133/1.2923185FiguresReferencesRelatedDetailsCited byEffect of bird maneuver on frequency‐domain helicopter EM responseDavid V. Fitterman and Changchun Yin27 September 2004 | GEOPHYSICS, Vol. 69, No. 5Attitude corrections of helicopter EM data using a superposed dipole modelChangchun Yin and Douglas C. Fraser1 April 2004 | GEOPHYSICS, Vol. 69, No. 2 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 Changchun Yin and Greg Hodges, (2003), "Identification of Electrical Anisotropy from Helicopter EM Data," Symposium on the Application of Geophysics to Engineering and Environmental Problems Proceedings : 419-431. https://doi.org/10.4133/1.2923185 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.
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) | 0.000 | 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 it