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Record W2891394034 · doi:10.1190/segam2018-w20-02.1

Advances in DAS seismic monitoring for CO2 storage

2018· article· en· W2891394034 on OpenAlexaffabout
Don C. Lawton, Kevin Hall, Adriana Gordon

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicSeismic Waves and Analysis
Canadian institutionsUniversity of CalgaryCMC Research Institutes
Fundersnot available
KeywordsTrenchBoreholeGeologyDistributed acoustic sensingSchematicPresentation (obstetrics)AttenuationInversion (geology)SeismologyRemote sensingTelecommunicationsComputer scienceTectonicsEngineeringOptical fiberElectrical engineeringFiber optic sensorGeotechnical engineeringMaterials science

Abstract

fetched live from OpenAlex

The Containment and Monitoring Institute (CaMI) Field Research Station (FRS) has been developed by CMC Research Institutes, Inc. and the University of Calgary in Newell County, Alberta. The goal of the FRS is to evaluate various technologies for early detection of the loss of containment associated with the geological storage of CO2. At the site, CO2 is injected into a sandstone reservoir at a depth of 300 m below the surface. Permanent borehole sensors in a monitoring well include straight and helical wound optical fibre for distributed acoustic sensing (DAS) surveys. In addition, 1.1 km of straight and helical wound cable have been buried in a 1 m deep trench close to the monitoring well. A schematic diagram of the fibre layout is shown in Figure 1. Presentation Date: Start Time: Location: Presentation Type:

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation 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: none
Teacher disagreement score0.010
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0030.002
Science and technology studies0.0000.001
Scholarly communication0.0020.002
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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.

Opus teacher head0.015
GPT teacher head0.270
Teacher spread0.255 · 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 source (direct Gemma or distilled Codex), 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

Citations8
Published2018
Admission routes2
Has abstractyes

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