Liming for the mitigation of acid rain effects in freshwaters: A review of recent results
Bibliographic record
Abstract
Acid rain has affected freshwater ecosystems for more than 50 years in much of northern Europe and North America. The acidification of waters, along with concurrent reduction in acid neutralization capacity, has caused deleterious changes to aquatic populations in much of these regions. To reverse some of the changes to aquatic ecosystems, a number of governmental and nongovernmental groups have applied lime and other neutralizing substances to streams, rivers, lakes, and catchments in the most affected or most ecologically valuable regions. We review the scientific literature published since the late 1980s on liming to provide an overview of successes and failures of various approaches. We discuss the rationale behind liming programs and why certain approaches may not be helpful in mitigating acidification effects under varying conditions. One of our main conclusions is that though water chemistry may be restored if only temporarily, aquatic communities probably will not return to their original states, though targeted fish species can be restored using active management approaches. The communities restored, however, are usually more unstable than those from undisturbed, or pre-acidification conditions. We also show that liming may have to be conducted for 50 to 60 years in some affected locations, which should affect the choice of approaches used in mitigation.Key words: acid rain, mitigation, liming, freshwaters, catchments, salmonids.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".