A nutrient budget for a selection harvest: implications for long-term sustainability
Bibliographic record
Abstract
Declining concentrations of Ca and other base cations in soils and surface waters in eastern North America have led to concerns that forests may become nutrient limited, particularly in regions that are harvested. We constructed a nutrient budget for a selection harvest in central Ontario that is typical of eastern North America. Atmospheric deposition (5-year average) and mineral weathering (PROFILE) were considered as the sole inputs to the forest, while exports included nutrient losses in streams and removed in stems. Sugar maple ( Acer saccharum Marsh.) was the only tree species removed in the study (∼30% of basal area) and harvesting had no strong impact on stream chemistry. Mass balance calculations were performed on average values, but with estimates of uncertainty associated with each input parameter. A Monte Carlo simulation was run (10 000 runs) for Ca, Mg, K, Na, P, N, and S. In the absence of harvesting, average mass balance estimates are positive for all nutrients except S. When harvesting is considered, average mass balances remain positive for all nutrients except Ca, K, and S. Monte Carlo simulations demonstrate that mass balances for Na are always positive, while mass balances for Mg, K, N, P, and S range from slightly positive to slightly negative. In contrast, mass balance simulations for Ca are always negative and average net losses represent ∼1% of the current exchangeable soil Ca pool.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".