Establishing a Rural Groundwater Monitoring Network Using Existing Wells: West Nose Creek Pilot Study, Alberta
Bibliographic record
Abstract
Sustainable groundwater management requires long-term monitoring of aquifer water level, water quality, and water use with adequate spatial and temporal resolution in order to evaluate the response of the aquifer to changes in pumping rates and meteorological conditions. Since existing federal and provincial monitoring programs do not have sufficient spatial resolution, an alternative is to establish locally-based monitoring programs coordinated by municipalities or watershed groups. A network of more than 20 monitoring wells was implemented in the West Nose Creek watershed near Calgary, Alberta using existing water supply wells. The network effectively captured the pattern of seasonal and inter-annual fluctuations of aquifer water level. Understanding of the natural fluctuation will help the community detect any undesirable effects of increasing water extraction in the future. Biannual newsletters were distributed to the well owners and a wider community within the watershed to communicate the results and background knowledge. The methodology established in this pilot study may provide a cost-effective tool for rural groundwater monitoring in the Canadian prairies and elsewhere.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".