Novel zooplankton community compositions in lakes that have recovered from acidification in Sudbury, Ontario
Bibliographic record
Abstract
Biomonitoring has repeatedly been proven to be an effective tool in assessing aquatic ecosystems and is especially useful in historically stressed environments like Sudbury area lakes, that are on an acid-recovery trajectory while facing contemporary changes including urbanization, calcium declines, climate change and lake brownification. Due to the varied ranges of sensitivities among different species, zooplankton are important bioindicator taxa of pelagic waters often used in biomonitoring. Their crucial link between primary producers and fish communities also make them important taxa for monitoring. After decades of water chemistry improvements in Sudbury, crustacean zooplankton communities have sometimes lagged in recovery that may be attributable to factors such as dispersal, residual metal toxicity, biotic resistance, and incomplete food webs. I conducted a broad spatial survey of 58 historically acidified lakes and 24 reference lakes in the Sudbury acid deposition zone and 5 remote reference lakes to understand the current variation in pelagic zooplankton community compositions in lakes along numerous gradients of change to and to determine the recovery status of zooplankton communities. My results show that zooplankton communities in lakes that have chemically recovered from historical acidification do not resemble reference compositions after over four decades since emission reductions, suggesting a shift to alternate community compositions due to changing contemporary environmental factors.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".