Limnology in northeastern Ontario: from acidification to multiple stressorsThis Perspective is based on the author’s F.H. Rigler Lecture delivered at the annual meeting of the Society of Canadian Limnologists in Halifax, Nova Scotia, January 2008.
Bibliographic record
Abstract
Thousands of lakes around Sudbury, in northeastern Ontario, Canada, were badly damaged by acid deposition and many were also metal-contaminated. Large reductions in atmospheric sulphur and metal emissions have led to widespread chemical improvements in these lakes, and recovery has been documented for various biota. These findings were very important in establishing the necessity and value of sulphur emission controls during the international debates about the effects of acid deposition and the need for cleaner air. Studies of northeastern Ontario lakes are continuing to advance our understanding of chemical and biological recovery processes; however, that knowledge is still incomplete. It has become apparent that the recovery of lakes from acidification is closely linked with the responses to, and interactions with, other large-scale environmental stressors like climate change and calcium declines. Developing a better understanding of lake recovery processes and their future outcomes within such a multiple stressor context will be difficult. It will demand the merging of various approaches, including monitoring, experimentation, paleolimnology, and modelling, and will require effective collaboration among different research and monitoring sites and various agencies and institutions engaged in environmental science.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".