Natural controls on phosphorus concentrations in small Lakes in Central Alberta, Canada
Bibliographic record
Abstract
Small lakes can act as environmental sensors for understanding regional hydrogeological conditions and elemental cycles. Because of their small size, they react quickly to changes in nutrient runoff, groundwater exchange and climate. In the post-glacial landscape of Alberta, the Carvel Pitted Delta harbours a variety of small lakes, which, despite being close together, can have markedly different chemistries and nutrient characteristics. Here, we focused on nine lakes with surface areas <10 ha and maximal depths ranging from 5 to 18 m. We hypothesised that differences in their trophic states are caused by variations in Ca concentrations, controlled by groundwater inputs. All lakes tended toward permanent stratification due to small mixing depths (1.6–2.4 m). The deeper lakes were oligotrophic with high concentrations of Ca and a high fraction of phosphorus (Ca-bound P) in the sediments (HCl extractable fraction). A correlation was found between Ca-bound P and aqueous Ca concentrations at 0.5 m depth, suggesting that P stability could be estimated based on surface water chemistry. Calcium was shown to be concentrated in groundwater located above the bedrock, suggesting that groundwater inputs controlled Ca concentrations in the lakes. We conclude that the hydrogeological conditions and Ca inputs act as natural regulators for P availability and, by extension, water quality in these lakes.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.009 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".