The use of ground penetrating radar in permafrost environments
Bibliographic record
Abstract
PreviousNext No AccessSEG Technical Program Expanded Abstracts 2000The use of ground penetrating radar in permafrost environmentsAuthors: Stephen D. RobinsonMargo M. BurgessInez M. KettlesStephen A. WolfeStephen D. RobinsonGeological Survey of Canada, Margo M. BurgessGeological Survey of Canada, Inez M. KettlesGeological Survey of Canada, and Stephen A. WolfeGeological Survey of Canadahttps://doi.org/10.1190/1.1815652 SectionsAboutPDF/ePub ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinked InReddit Permalink: https://doi.org/10.1190/1.1815652FiguresReferencesRelatedDetailsCited byMapping vertical profile of discontinuous permafrost with ground penetrating radar at Nalaikh depression, Mongolia15 March 2008 | Environmental Geology, Vol. 56, No. 8 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 Stephen D. Robinson, Margo M. Burgess, Inez M. Kettles, and Stephen A. Wolfe, (2000), "The use of ground penetrating radar in permafrost environments," SEG Technical Program Expanded Abstracts : 1363-1366. https://doi.org/10.1190/1.1815652 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".