Analysis of the annual thermal response of an earth dam for the assessment of the hydraulic conductivity of its compacted till core
Bibliographic record
Abstract
Heat carried by the seepage water along hydraulic flow lines can serve as a natural tracer to help detect contrasting hydraulic conductivities in embankment dams. Thermal monitoring was realized on the 94.5 m high QA-01 embankment dam in northern Quebec to characterize the temperature distribution across its entire section. The analysis of the annual thermal response of the dam clearly showed a zone of higher seepage velocities in the compacted till core. Approximations concerning the increased hydraulic conductivity were made using a simple thermal model. Complementary numerical modelling provided a more rigorous quantitative assessment of the seepage patterns. The calculations have shown hydraulic conductivities that are 20 times larger than the expected values. Thermal monitoring can detect zones of increased seepage, which can be related to internal erosion or other factors, such as the variability of the soil properties due to construction practices. Temperature measurements alone are not adequate to determine the cause of a permeable zone in a dam or to predict its possible evolution.
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 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 | 0.000 | 0.000 |
| 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".