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Record W2610177470 · doi:10.1139/facets-2017-0013

Strategies to prevent damage to critical infrastructure due to induced seismicity

2017· article· en· W2610177470 on OpenAlexafffundvenueabout
Gail M. Atkinson

Bibliographic record

VenueFACETS · 2017
Typearticle
Languageen
FieldEngineering
TopicSeismic Performance and Analysis
Canadian institutionsWestern University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsInduced seismicityCritical infrastructureSeismic hazardHazardEnvironmental scienceComputer scienceSeismologyGeologyComputer security

Abstract

fetched live from OpenAlex

There has been a significant increase in the rate of felt earthquakes in western Alberta and eastern British Columbia, which has been associated with hydraulic fracturing and wastewater disposal. The increased rate of seismicity and the potential for localized strong ground motions from very shallow events poses an increased hazard to critical infrastructure such as major dams—particularly for older high-consequence structures. This paper overviews the factors that affect the likelihood of damaging ground motions and examines their implications for hazard assessment and mitigation. A strategy aimed at reducing the likelihood of potentially damaging ground motions to achieve probabilistic targets for critical facilities is developed, comprising elements of both mitigation and avoidance. For critical facilities, an effective strategy includes (i) an exclusion zone having a radius of ∼5 km; and (ii) a monitoring-and-response protocol to track the rate of events at the M > 2 level within 25 km, with adjustment of operational practices if required. An exclusion zone provides a deterministic safety margin to ensure the integrity of those few facilities for which failure consequences are unacceptable. Real-time monitoring tied to a response protocol can be used to control the rate of significant events and thereby limit the hazard more broadly.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.545
Threshold uncertainty score0.614

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.014
GPT teacher head0.284
Teacher spread0.271 · 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 designSimulation or modeling
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

Citations19
Published2017
Admission routes4
Has abstractyes

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