A study of near‐surface seasonal variability using Rayleigh wave dispersion
Bibliographic record
Abstract
PreviousNext No AccessSEG Technical Program Expanded Abstracts 2000A study of near‐surface seasonal variability using Rayleigh wave dispersionAuthors: K. S. BeatyD. R. SchmittK. S. BeatyInstitute for Geophysical Research, Department of Physics, University of Alberta and D. R. SchmittInstitute for Geophysical Research, Department of Physics, University of Albertahttps://doi.org/10.1190/1.1815641 SectionsAboutPDF/ePub ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinked InRedditEmail Permalink: https://doi.org/10.1190/1.1815641FiguresReferencesRelatedDetailsCited byActive and Passive Seismic as an Indicator of Large Equipment Interactions with the Oil Sand30 May 2010 | Geotechnical and Geological Engineering, Vol. 28, No. 6Investigation of the near subsurface using acoustic to seismic couplingEcohydrology, Vol. 2, No. 3Role of Forward Model in Surface-Wave Studies to Delineate a Buried High-Velocity LayerXiaohui Jin, Barbara Luke, and Carlos Calderón-Macéas21 June 2012 | Journal of Environmental and Engineering Geophysics, Vol. 14, No. 1Inversion of shallow-seismic wavefields: I. Wavefield transformationGeophysical Journal International, Vol. 153, No. 3Repeatability of multimode Rayleigh‐wave dispersion studiesKristen S. Beaty and Douglas R. Schmitt29 May 2003 | GEOPHYSICS, Vol. 68, No. 3Simulated annealing inversion of multimode Rayleigh wave dispersion curves for geological structureGeophysical Journal International, Vol. 151, No. 2 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 K. S. Beaty and D. R. Schmitt, (2000), "A study of near‐surface seasonal variability using Rayleigh wave dispersion," SEG Technical Program Expanded Abstracts : 1323-1326. https://doi.org/10.1190/1.1815641 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) | 0.036 | 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".