Bibliographic record
Abstract
PreviousNext No AccessSEG Technical Program Expanded Abstracts 2001Seismic interpretation of sonic logsAuthors: Jonathan BorkLawrence C. WoodJonathan BorkApache Corporation and Lawrence C. WoodGeophysical Consulting Inc.https://doi.org/10.1190/1.1816660 SectionsAboutPDF/ePub ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinked InRedditEmail Permalink: https://doi.org/10.1190/1.1816660FiguresReferencesRelatedDetailsCited ByMultichannel Semi-blind Deconvolution (MSBD) of seismic signalsSignal Processing, Vol. 135A prestack basis pursuit seismic inversionRui Zhang, Mrinal K Sen, and Sanjay Srinivasan10 December 2012 | GEOPHYSICS, Vol. 78, No. 1A pre-stack basis pursuit seismic inversionRui Zhang, Mrinal K Sen, and Sanjay Srinivasan25 October 2012Seismic sparse-layer reflectivity inversion using basis pursuit decompositionGEOPHYSICS, Vol. 76, No. 6 SEG Technical Program Expanded Abstracts 2001ISSN (print):1052-3812 ISSN (online):1949-4645Copyright: 2001 Pages: 2135 publication data© 2001 Copyright © 2001 Society of Exploration GeophysicistsPublisher:Society of Exploration Geophysicists HistoryPublished: 03 Jan 2005 CITATION INFORMATION Jonathan Bork and Lawrence C. Wood, (2001), "Seismic interpretation of sonic logs," SEG Technical Program Expanded Abstracts : 510-513. https://doi.org/10.1190/1.1816660 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 0.006 |
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".