Continuous Monitoring of an Ice Sheet in a Reservoir Upstream of Beaumont Dam, Canada
Bibliographic record
Abstract
The geomatics engineering contributions to continuous three-dimensional monitoring of ice sheet in a dam reservoir is presented in this paper. A total station robot was used to monitor the displacement of several probes placed on the surface of the ice sheet of a dam reservoir during winter seasons. Two prisms were mounted on each probe to take into account the deflection variation of the probe during a complete winter season. A similarity (Helmert) transformation was computed from measurements on control points to solve the problem of inconsistent observations. The results of the 2009 and 2010 winter campaigns at the Beaumont Dam are reported in this paper. We show that the horizontal displacements of the ice sheet, which can be as large as 20–30 cm, can vary quite differently from one winter season to another one, as a function of the temperature and its variation during winter. The horizontal displacements also depend on the probe's distance from the dam or from the reservoir banks. Vertical displacements of the ice sheet follow the reservoir water level fluctuations, especially for the probes far from the dam and reservoir banks. Three-dimensional displacements are explained by the gradual increase of the ice sheet’s thickness, the snowpack accumulation, and the local constraints (hinge effects) near the reservoir perimeter.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".