Assessing the effectiveness of hydraulic fractures with microseismicity
Bibliographic record
Abstract
PreviousNext No AccessSEG Technical Program Expanded Abstracts 2003Assessing the effectiveness of hydraulic fractures with microseismicityAuthors: Ted UrbancicShawn MaxwellRichard ZinnoTed UrbancicEngineering Seismology Group, Kingston, ON, CanadaSchlumberger, Houston, Tx, Shawn MaxwellEngineering Seismology Group, Kingston, ON, CanadaSchlumberger, Houston, Tx, and Richard ZinnoSchlumberger, Houston, Txhttps://doi.org/10.1190/1.1817923 SectionsAboutPDF/ePub ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinked InRedditEmail Permalink: https://doi.org/10.1190/1.1817923FiguresReferencesRelatedDetailsCited ByStress transfer from opening hydraulic fractures controls the distribution of induced seismicity10 December 2019 | Journal of Geophysical Research: Solid EarthA statistical model for the seismicity rate of fluid-injection-induced earthquakes15 September 2004 | Geophysical Research Letters, Vol. 31, No. 17 SEG Technical Program Expanded Abstracts 2003ISSN (print):1052-3812 ISSN (online):1949-4645Copyright: 2003 Pages: 2452 publication data© 2003 Copyright © 2003 Society of Exploration GeophysicistsPublisher:Society of Exploration Geophysicists HistoryPublished: 03 Jan 2005 CITATION INFORMATION Ted Urbancic, Shawn Maxwell, and Richard Zinno, (2003), "Assessing the effectiveness of hydraulic fractures with microseismicity," SEG Technical Program Expanded Abstracts : 366-369. https://doi.org/10.1190/1.1817923 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 | 0.003 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".