Management of groundwater resources in the coastal aquifers of the Magdalen Islands (Canada) using decision support models
Bibliographic record
Abstract
Abstract Freshwater resources are scarce on islands surrounded by seawater, where groundwater lenses are often the only sources of fresh water. These resources are highly sensitive to climate variability and human activities, requiring specialized management approaches. Here, groundwater flow models that account for climate and parameter uncertainty are used to guide groundwater resource management on the Magdalen Islands (Quebec, Canada). An island-wide groundwater flow model was developed for four islands of the archipelago using MODFLOW-2005 and the sharp interface seawater intrusion package SWI2, driven by a spatially distributed SWB2 recharge model. Parameter estimation was then conducted using PEST_HP, producing island-wide maps of the freshwater lenses and water budgets. Transient simulations were run to determine the percent rise of the freshwater–seawater interface below pumping wells relative to the onset of pumping. The model was then combined with PESTPP-OPT and climate change projections to conduct pumping optimization under climate and parameter uncertainty, and with MODPATH to delineate zones for groundwater protection. The modeling results indicate that groundwater resources will be sufficient to meet the future water demand on the islands as projected through 2050. While some wellfields can provide more fresh water, others cannot, and certain wells may be at risk of saltwater intrusion in the future. Climate change is unlikely to impact overall groundwater resource availability, but it will reduce the volume of freshwater that can be withdrawn from existing wellfields. The scripted, open-source modeling approach used here can be applied to similar environments to address common management challenges encountered in island aquifer settings.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".