Crustacean zooplankton communities in lakes recovering from acidification
Bibliographic record
Abstract
Large reductions in sulphur emissions at the Sudbury, Ont., Canada, smelters in recent decades have resulted in decreased lake acidity, and biological improvements have followed. Lakes in the Sudbury area offer a very unique opportunity to develop our understanding of the processes regulating biological restructuring in aquatic ecosystems recovering from acidification. Here, we examine changes in crustacean zooplankton communities that have accompanied the chemical recovery of Whitepine and Sans Chambre lakes, near Sudbury, over the last two decades. In both these formerly acidic lakes, pH has increased to ~6.0, and some zooplankton community recovery has occurred. However, zooplankton communities have not completely recovered based on multivariate comparisons with the community composition of reference lakes. Although a number of acid-sensitive species have appeared, many did not persist, or did not achieve abundances typical of the reference lakes. This indicates that zooplankton community recovery will most likely depend on biotic and abiotic interactions within these lakes and not on factors affecting species dispersal. Both chemical and biological factors have large influences on biological recovery processes. Assessing biological recovery is very important since the restoration of healthy aquatic communities is a major objective of large-scale sulphur emission control programs.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 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".