Fiber optic distributed temperature sensing of soil moisture in waste rock
Bibliographic record
Abstract
One of the most challenging aspects of controlling contamination in mining operations is the management of tailings and waste rock piles. Waste rock is the material extracted from a mine that has no economic value and may be a source of contamination from acid or neutral mine drainage. Only in recent decades have mining operations incorporated covers in the design of waste rock piles to prevent the infiltration of water and oxygen, increase chemical stabilization, and minimize contaminant leachate. This thesis focuses on the distributed measurement of soil moisture in a waste rock pile using active fiber optic distributed temperature sensing (aFO-DTS). Soil moisture is measured by heating the metal casing of a fiber optic cable and evaluating the thermal response. This emerging geophysical method allows for the measurement of soil moisture continuously along several kilometers of fiber optic cable, with measurement spacing every half meter, and a temporal resolution of less than an hour per measurement. In this thesis, I undertake three projects: (1) A laboratory column experiment in which the fiber optic cable was buried in two layers of soils with different hydrogeologic properties. By using aFO-DTS, I evaluate the spatial resolution of soil moisture in the column and at the interface between the two layers of soil using the created protocol. (2) I apply my new aFO-DTS method to a constructed test waste-rock pile at the Lac Tio mine in eastern Quebec. The test pile is using a new capillary barrier cover design. By using aFO-DTS, I assess the temporal distribution of soil moisture at different depths within the pile (3). I test uncertainties and common assumptions used by aFO-DTS methods using a numerical model. To date, there has been very little analysis of the potential errors related to subsurface heterogeneity and method calibration. The rate and period of recharge are found to be among the largest potential sources of error and require careful calibration when using the aFO-DTS method
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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 | 0.000 | 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) | 0.000 | 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".