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Record W2108278931 · doi:10.1785/0220150048

Characterizing and Responding to Seismic Risk Associated with Earthquakes Potentially Triggered by Fluid Disposal and Hydraulic Fracturing

2015· article· en· W2108278931 on OpenAlexfundno aff
Randi Walters, Mark D. Zoback, Jack W. Baker, Gregory C. Beroza

Bibliographic record

VenueSeismological Research Letters · 2015
Typearticle
Languageen
FieldEarth and Planetary Sciences
Topicearthquake and tectonic studies
Canadian institutionsnot available
FundersRoyal SocietyRoyal Academy of EngineeringCanadian Association of Petroleum Producers
KeywordsGeology

Abstract

fetched live from OpenAlex

Research Article| June 10, 2015 Characterizing and Responding to Seismic Risk Associated with Earthquakes Potentially Triggered by Fluid Disposal and Hydraulic Fracturing Randi Jean Walters; Randi Jean Walters aDepartment of Geophysics, Stanford University, 397 Panama Mall B59, Stanford, California 94305 U.S.Awalters1@stanford.edu Search for other works by this author on: GSW Google Scholar Mark D. Zoback; Mark D. Zoback bDepartment of Geophysics, Stanford University, 397 Panama Mall Room 347, Stanford, California 94305 U.S.A Search for other works by this author on: GSW Google Scholar Jack W. Baker; Jack W. Baker cDepartment of Civil and Environmental Engineering, Stanford University, 473 Via Ortega Room 283, Stanford, California 94305 U.S.A Search for other works by this author on: GSW Google Scholar Gregory C. Beroza Gregory C. Beroza dDepartment of Geophysics, Stanford University, 397 Panama Mall Room 355, Stanford, California 94305 U.S.A Search for other works by this author on: GSW Google Scholar Author and Article Information Randi Jean Walters aDepartment of Geophysics, Stanford University, 397 Panama Mall B59, Stanford, California 94305 U.S.Awalters1@stanford.edu Mark D. Zoback bDepartment of Geophysics, Stanford University, 397 Panama Mall Room 347, Stanford, California 94305 U.S.A Jack W. Baker cDepartment of Civil and Environmental Engineering, Stanford University, 473 Via Ortega Room 283, Stanford, California 94305 U.S.A Gregory C. Beroza dDepartment of Geophysics, Stanford University, 397 Panama Mall Room 355, Stanford, California 94305 U.S.A Publisher: Seismological Society of America First Online: 14 Jul 2017 Online ISSN: 1938-2057 Print ISSN: 0895-0695 © 2015 by the Seismological Society of America Seismological Research Letters (2015) 86 (4): 1110–1118. https://doi.org/10.1785/0220150048 Article history First Online: 14 Jul 2017 Cite View This Citation Add to Citation Manager Share Icon Share Facebook Twitter LinkedIn MailTo Tools Icon Tools Get Permissions Search Site Citation Randi Jean Walters, Mark D. Zoback, Jack W. Baker, Gregory C. Beroza; Characterizing and Responding to Seismic Risk Associated with Earthquakes Potentially Triggered by Fluid Disposal and Hydraulic Fracturing. Seismological Research Letters 2015;; 86 (4): 1110–1118. doi: https://doi.org/10.1785/0220150048 Download citation file: Ris (Zotero) Refmanager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentBy SocietySeismological Research Letters Search Advanced Search For nearly a century, earthquakes apparently triggered by fluid injection have been observed in many parts of the world (National Research Council [NRC], 2012). Although injection‐related seismicity is a well‐known phenomenon, recent years have seen a dramatic increase in earthquake occurrence apparently associated with oil and gas development. This increase has been most notable in the central and eastern United States (Ellsworth, 2013). Recent occurrences of felt events in areas of significant populations have brought attention to this issue from the public, oil and gas operators, regulators, and academics. Though fluid disposal and hydraulic fracturing both... You do not have access to this content, please speak to your institutional administrator if you feel you should have access.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.214
Threshold uncertainty score0.618

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.043
GPT teacher head0.275
Teacher spread0.233 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations102
Published2015
Admission routes1
Has abstractyes

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