Detection of seepages and monitoring of structural changes in earthen embankments by FO DTS
Bibliographic record
Abstract
The aim of this paper is to describe an innovation in the monitoring of performance of earth embankments, namely the use of sensing systems for full-time monitoring of ash pond and irrigation pond seepages.In the past decades, due to IT development and cost reduction, the sensor technology segment and associated measurement and monitoring systems have started to expand rapidly.One of the areas of sensor technology suitable for real-time monitoring of earthen embankments is that of fibre optic distributed temperature sensors (FO DTS).This paper deals with the results of pilot measurements performed within the E!11705 FORMTES project in 2018 -2020, as related to the analysis of the existing FO DTS system installed in the irrigation pond in Spain, and on the new implementation of the FO DTS system in part of the embankment of the ash pond in Hungary, and the use of FO DTS systems for monitoring of potential seepage in above mentioned embankment structures.The evaluated pilot tests together with the desk review were used for the validation and enhancement of the novel methodology to enable its real-life implementation both for the existing embankment structures and the development of detailed designs of new structures of similar nature.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".