Ecological benefits and risks arising from liming sugar maple dominated forests in northeastern North America
Bibliographic record
Abstract
Liming, the application of carbonate materials (e.g., CaCO3, CaMg(CO3)2) to soils and surface waters, has been used extensively in Europe, and to a lesser extent in Canada and the United States, to mitigate the effects of acid deposition on forest and aquatic ecosystems. This literature review was conducted to assess the effects of liming on ecologically and economically important sugar maple dominated ecosystems of northeastern North America, where it is increasingly used to treat sugar maple dieback. Potential direct and indirect effects were considered to determine whether the use of liming to revitalize these forests could negatively affect other ecological parameters, including those in adjacent aquatic habitats. Based on current scientific literature, it is not anticipated that liming at rates of 1–3 t ha−1would have major detrimental effects on these ecosystems. However, liming could have negative effects on northern hardwood forests with regard to earthworm invasions. The choice of liming as a mitigation tool should be made not only after weighing the potentially negative effects against the benefits of restoring sugar maple dominated stands in poorly buffered soils, but also after considering ecological components that could be lost or never recovered if an acidified forest ecosystem is not limed.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".