Recovery of acidified Sudbury, Ontario, Canada, lakes: a multi-decade synthesis and update
Bibliographic record
Abstract
The Sudbury region of northeastern Ontario, Canada, provides one of the world’s best examples of the resilience of aquatic ecosystems after reductions in atmospheric contaminant deposition. Thousands of lakes around the Sudbury metal smelters were badly damaged by acid deposition. Lakes closest to the smelters were also contaminated by metal particulates. However, large reductions in atmospheric SO2and metal emissions starting in the early 1970s have led to widespread chemical improvements in these lakes, and recovery has been observed for various aquatic biota. Studies of Sudbury-area lakes are advancing our understanding of chemical and biological lake recovery; however, recovery is a complicated process and much remains to be learned. Biological recovery has often been slow to follow chemical recovery, and it has become apparent that the recovery of lakes from acidification is closely linked to interactions with other large-scale environmental stressors like climate change and Ca declines. Thus, in our multiple-stressor world, recovery may not bring individual lakes back to their exact former state. However, with time, substantial natural biological recovery toward typical lake communities can be reasonably expected for most but not necessarily all biota. For organisms with limited dispersal ability, particularly fish, human assistance may be necessary to re-establish typical communities. In lakes where food webs have been severely altered, re-establishment of typical diverse fish communities may in fact be an important element aiding the recovery of other important components of aquatic ecosystems including zooplankton and benthic macroinvertebrates. In the lakes closest to the smelters, where historically watersheds as well as lakes were severely damaged, the recovery of aquatic systems will be closely linked to ongoing terrestrial recovery and rehabilitation, particularly through the benefits of increased inputs of terrestrially derived organic matter. The dramatic lake recovery observed in the Sudbury area points to a brighter future for these lakes. However, continued monitoring will be needed to determine future changes and help guide the management and protection of Sudbury-area lakes in this multiple-stressor age.
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 | 0.007 | 0.017 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.017 | 0.034 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".