Relative contributions of stream concentration, stream discharge and shoreline load to base cation trends in Red Chalk and Harp lakes, south-central Ontario, Canada
Bibliographic record
Abstract
We evaluated the importance and contributions of changes in stream base cation concentration, stream discharge and lake shoreline load to changes in base cation concentrations in two small inland lakes in south-central Ontario. The shoreline load from ungauged drainage areas was calculated with a mass balance equation. An evaluation method based on the partial Mann–Kendall test quantified the relative contributions of these three explanatory drivers to the observed trends in lake concentration of Ca, K, Mg and Na. Over a 29-year period (1978–2006) at Red Chalk and Harp lakes, declines in stream concentration and discharge were correlated with the declines in lake concentrations of all base cations (except for Na in Harp Lake), with contributions of 21–81% from stream concentration and 12–58% from discharge, whereas the shoreline load had little contribution to observed trends. The observed unusual increase in Na concentration at Harp Lake was correlated with the increase in stream Na concentration and additional load from road salts, with the shoreline load contributing a substantial 37%. These results may be applicable to numerous inland lakes with similar site conditions, including lakes found on the Canadian Shield. Copyright © 2015 John Wiley & Sons, Ltd.
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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 | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".