Nearshore human interventions reverse patterns of decline in lake calcium budgets in central Ontario as demonstrated by mass‐balance analyses
Bibliographic record
Abstract
Calcium (Ca) has declined to levels threatening aquatic biota in lakes on the eastern Canadian Shield. Predictive models for future changes in lake Ca are generally based on catchment‐scale studies, but these models rarely account for unmeasured sources of Ca supply that are common in the nearshore areas of developed lakes. In this study we utilize up to 29 years of hydrological and water chemistry data for three lakes in central Ontario that differ in degree of human intervention to demonstrate that shoreline development may exert large effects on Ca mass balances. In the relative absence of shoreline development, Red Chalk Lake exhibited what we consider to be the normal response, a reduction in Ca load from the catchment over the last three decades, leading to a reduction in lake export and lake Ca concentration. Calcium load, export, and lake water Ca concentration also fell in Harp Lake, but less than in Red Chalk Lake, because Ca loads were elevated by human activities in Harp Lake's moderately developed shoreline area. By contrast, Dickie Lake experienced an exceptional change in Ca dynamics: both export and lake concentrations rose because of elevated load from the shoreline area linked to the use of dust suppressants on gravel roads. Reductions in both stream Ca concentration and flow volume have led to calcium decline in streams and lakes. Long‐term soil acidification processes and climatic variability with its link to hydrology can explain the general pattern of Ca decline in lakes on the south‐central Canadian Shield. However, given the widespread lakeshore development and use of dust suppressants on gravel roads, predictions of lake Ca levels need to take into account nearshore activities, especially those that augment rates of Ca supply.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".