Application of Remote Sensing and Airborne Geophysics to Mine Tailings Monitoring, Copper Cliff, Ontario
Bibliographic record
Abstract
PreviousNext No AccessSymposium on the Application of Geophysics to Engineering and Environmental Problems 2002Application of Remote Sensing and Airborne Geophysics to Mine Tailings Monitoring, Copper Cliff, OntarioAuthors: Bill MorrisJiali ShangPhilip HowarthKen WitherlyBill MorrisApplied Geophysics Group, School of Geography and Geology, McMaster University Hamilton, Ontario, Jiali ShangWaterloo Laboratory for Earth Observations, Department of Geography, University of Waterloo, Waterloo, Ontario, Canada N2L 3G1, Philip HowarthWaterloo Laboratory for Earth Observations, Department of Geography, University of Waterloo, Waterloo, Ontario, Canada N2L 3G1, and Ken WitherlyCondor Consulting Inc., St 206, 4860 Robb Street, Wheat Ridge, CO 80033https://doi.org/10.4133/1.2927142 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/10.4133/1.2927142FiguresReferencesRelatedDetailsCited ByElectrical Resistivity Imaging Applied to Tailings Ponds: An Overview5 January 2021 | Mine Water and the Environment, Vol. 40, No. 1 Symposium on the Application of Geophysics to Engineering and Environmental Problems 2002ISSN (online):1554-8015Copyright: 2002 Pages: publication data© 2002 Copyright © 2002 The Environmental and Engineering Geophysical SocietyPublisher:Environmental & Engineering Geophysical Society HistoryPublished: 30 Sep 2008 CITATION INFORMATION Bill Morris, Jiali Shang, Philip Howarth, and Ken Witherly, (2002), "Application of Remote Sensing and Airborne Geophysics to Mine Tailings Monitoring, Copper Cliff, Ontario," Symposium on the Application of Geophysics to Engineering and Environmental Problems Proceedings : MMM1-MMM1. https://doi.org/10.4133/1.2927142 Plain-Language Summary PDF DownloadLoading ...
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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) | 0.000 | 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".