MétaCan
Menu
Back to cohort
Record W2620940893 · doi:10.1061/9780784480717.003

Deformation Monitoring for the Assessment of Sacramento Delta Levee Performance

2017· article· en· W2620940893 on OpenAlexaff
Victoria Bennett, Cathleen E. Jones, David Bekaert, Jason Bond, Amr Helal, J. Dudas, Mohammed A. Gabr, Tarek Abdoun

Bibliographic record

VenueGeo-Risk 2017 · 2017
Typearticle
Languageen
FieldEngineering
TopicSynthetic Aperture Radar (SAR) Applications and Techniques
Canadian institutionsThe Audio Recording Academy
Fundersnot available
KeywordsLeveeSubsidenceDeformation (meteorology)GeologyPeatRemote sensingSettlement (finance)Deformation monitoringFoundation (evidence)Geotechnical engineeringEnvironmental scienceGeomorphologyComputer scienceGeography

Abstract

fetched live from OpenAlex

The Sacramento Delta levee network plays a vital role in supporting the United States’ largest agricultural production and the municipal water supply for millions of people. Beyond the potential for earthquake-induced failure, performance monitoring of the levee system on the network level and the assessment of potential distress levels are needed for the maintenance and rehabilitation of this system. In this case, critical deformation needs to be assessed especially in view of the levees age, sea level rise, and ongoing subsidence due to the decomposition of the peat foundation layer. The work presented herein describes the remote sensing of a levee section deformation with time. The levee section is located on Sherman Island, CA, and monitoring was conducted in situ by GPS, in addition to remote sensing by airborne synthetic aperture radar and satellite systems. Coupled deformation/seepage numerical analysis of the levee section is performed using PLAXIS. The numerical model included characterization of the unsaturated-saturated zones and the peat foundation layer. Key parameters of the peat layer were synthesized from published data to provide baseline assessment for the current condition of the levee system in view of the measured rate of deformation.

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: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.977
Threshold uncertainty score0.390

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 studies0.0010.000
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 head0.024
GPT teacher head0.298
Teacher spread0.274 · 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 designOther design
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

Citations5
Published2017
Admission routes1
Has abstractyes

Explore more

Same venueGeo-Risk 2017Same topicSynthetic Aperture Radar (SAR) Applications and TechniquesFrench-language works237,207