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Record W2114357653 · doi:10.1061/40940(307)59

Water Leakage Detection Using Optical Fiber at the Peribonka Dam

2007· article· en· W2114357653 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEnvironmental Science
TopicWater Quality Monitoring Technologies
Canadian institutionsHydro-Québec
Fundersnot available
KeywordsLeakage (economics)Optical fiberComputer scienceMaterials scienceEnvironmental scienceTelecommunications

Abstract

fetched live from OpenAlex

A monitoring system based on temperature readings using fiber optic cables was designed for leakage detection through possible defects in the cutoff wall of the Peribonka main dam. The system is based on the heat pulse method to measure the apparent soil thermal resistivity. By combining a line heat source with a fiber optic distributed temperature measurement, local analysis of heat transfer can be performed to detect, locate and estimate the leak percolating velocity. Laboratory tests were carried out in clean granular sand having a hydraulic conductivity (kSat) of 10–3 m/s. The apparent thermal resistivity measured with the system decreased from 0.6 m-K/W to negligible values when the Darcy velocity of the percolating fluid was increased from 10–6 to 10–4 m/s. This shows the capacity of the proposed system for detecting leakage in these kinds of soils. Finite element modeling of the experiments shows the same trend. Three hybrid cables were installed in boreholes through the alluvial foundation of the Peribonka main dam by way of boreholes. The cutoff wall is now completed and preliminary measurements indicate no significant seepage.

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.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.013
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.002

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 head0.035
GPT teacher head0.271
Teacher spread0.236 · 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

Quick stats

Citations29
Published2007
Admission routes1
Has abstractyes

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