Estimating calcium weathering rates and future lake calcium concentrations in the Muskoka–Haliburton region of Ontario
Bibliographic record
Abstract
Calcium (Ca) concentrations in surface waters on the Precambrian Shield are determined primarily by the Ca weathering rate in soil, which requires extensive soil data that generally do not exist. From a water chemistry database comprising approximately 550 lakes in south-central Ontario, Canada, 130 lakes were selected with low Ca concentrations (Ca < 75 µmol·L–1). Calcium weathering is primarily dominated by silicate minerals such as plagioclase and hornblende, allowing the use of Ca–sodium (Na) ratios in lake water to estimate Ca weathering rates. Soil profile data at seven sites indicated that the Ca–Na ratio from mineral weathering is 0.86; correspondingly, Ca weathering rates in lakes ranged from 0.04 to 0.24 kmol·ha–1·year–1 (median of 0.09 kmol·ha–1·year–1). This compares with a range of 0.06–0.24 kmol·ha–1·year–1 (median of 0.14 kmol·ha–1·year–1) obtained using the steady-state water chemistry model. Using these methods to bound potential weathering rates, Ca concentrations in individual lakes at steady state are predicted to decline by 10%–40% compared with current values. Dynamic soil hydrochemical model predictions indicate that Ca concentrations in lakes will be considerably lower than these steady-state predictions within decades if timber harvesting occurs in the drainage basins.
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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 | 0.000 |
| 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".