A Community-Led Assessment to Identify Groundwater-Dependent Lakes in Parkland County (Alberta, Canada)
Bibliographic record
Abstract
Responding to a growing concern about impacts from anthropogenic activity on several dozen lakes, a group of citizens initiated and led a water quality sampling program that included characterizing groundwater dependence. The small lakes are located on hummocky glacial terrain near Edmonton, Alberta, Canada. A team of volunteers collected lake samples for a variety of limnological and ecological analyses to document lake health and trophic state, and collaborated with a university research group to identify groundwater dependence using specific environmental tracers (δ2H, δ18O, and 222Rn). Water chemistry and isotopic measurements are largely explained by the position of a lake within the local groundwater flow system. A simple metric to express the likelihood of groundwater dependence was calculated using the total dissolved solids (TDS), δ18O, and 222Rn values. Across the relatively small study area, a greater likelihood of groundwater dependence was determined for lakes located downgradient from an elevated recharge area. In contrast, where the water table was relatively flat, a lower likelihood of groundwater dependence was found. These results were similar to the spatial pattern of a trophic state, indicating that groundwater dependence may be one of the factors responsible for lake ecological status. The data generated by citizens and the knowledge gained about the hydrology of this area will help discussions between landowners and decision makers on how to best manage land use in this diverse landscape.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".