Groundwater modelling for supporting sustainable water management to avoid water usage conflict in Lanoraie peatland (Quebec, Canada)
Bibliographic record
Abstract
Wetlands, particularly peatlands, have historically been used for agricultural production, as exemplified by the Lanoraie peatland complex in the St. Lawrence Valley (Quebec, Canada). In this region, unlined artificial ponds located at the interface between the peat and the surrounding sandy substrate are used for agricultural irrigation. However, low water levels in these ponds, as well as in neighboring rivers, have led to irrigation deficits, especially during summer low-flow periods when water demand is at its peak. This situation poses the risk of water use conflicts and draining the peatland could irreversibly harm its ecological functions. A recent project assessed the impact of agricultural ponds on the hydrology of the peatland-river-aquifer system to support sustainable water management. A comprehensive monitoring program has successfully collected essential environmental data, including information on geology, river flows, and groundwater levels. Using these data, a groundwater flow model was developed for a small area of the peatland complex. The results showed that pumping from the ponds could partially dewater the peatland, thereby endangering its ecological integrity. Building on these findings, a new project aims to evaluate the hydrological and hydrogeological dynamics of the peatland, to assess the impacts of vegetation, water use, and climate changes on its hydrology, to develop indicators to guide sustainable water allocation, and to explore potential Nature-based solutions to mitigate the effects of pumping. Methodological advancements are planned to develop a modelling framework allowing to incorporate the impact of peatland afforestation while accounting for the high sensitivity of peat deposits to groundwater level fluctuations. The knowledge generated will directly support integrated water resource management in the region.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".