The impact of acid deposition and forest harvesting on lakes and their forested catchments in south central Ontario: a critical loads approach
Bibliographic record
Abstract
Abstract. The impact of acid deposition and tree harvesting on three lakes and their representative sub-catchments in the Muskoka-Haliburton region of south-central Ontario was assessed using a critical loads approach. As nitrogen dynamics in forest soils are complex and poorly understood, for simplicity and to allow comparison among lakes and their catchments, CLs (A) for both lakes and forest soils were calculated assuming that nitrate leaching from catchments will not change over time (i.e. a best case scenario). In addition, because soils in the region are shallow, base cation weathering rates for the representative sub-catchments were calculated for the entire soil profile and these estimates were also used to calculate critical loads for the lakes. These results were compared with critical loads obtained by the Steady State Water Chemistry (SSWC) model. Using the SSWC model, critical loads for lakes were between 7 and 19 meq m-2yr-1 higher than those obtained from soil measurements. Lakes and forests are much more sensitive to acid deposition if forests are harvested, but two acid-sensitive lakes had much lower critical loads than their respective forested sub-catchments implying that acceptable acid deposition levels should be dictated by the most acid-sensitive lakes in the region. Under conditions that assume harvesting, the CL (A) is exceeded at two of the three lakes and five of the six sub-catchments assessed in this study. However, sulphate export from catchments greatly exceeds input in bulk deposition and, to prevent lakes from falling below the critical chemical limit, sulphate inputs to lakes must be reduced by between 37% and 92% if forests are harvested. Similarly, sulphate leaching from forested catchments that are harvested must be reduced by between 16 and 79% to prevent the ANC of water draining the rooting zone from falling below 0 μeq l-1. These calculations assume that extremely low calcium leaching losses (9–27 μeq l-1) from forest soils can be maintained without any decrease in forest productivity. Calcium concentrations in the three lakes have decreased by between ∼10 and 25% over the past 20 years and calculations assume that calcium concentrations in lakes can fall to around 30% of their current values without any harmful effects on biota. Both these assumptions require urgent investigation. Keywords: acid deposition, calcium, critical loads, forests, harvesting, 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 | 0.000 | 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 | 0.001 | 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".