Linking changes in sedimentary Cladocera assemblages to limnological variables in 67 Sudbury (Ontario, Canada) lakes reflecting various degrees of metal smelter impacts
Bibliographic record
Abstract
Despite extensive records of chemical recovery, relatively little is known about recovery of aquatic biota in Sudbury (Ontario, Canada). Cladocera (Branchiopoda) are key components of lake food webs, and understanding spatial patterns in their assemblages may emphasize ecosystem recovery challenges. Paleolimnological techniques complement and provide long-term context to modern monitoring. Cladoceran remains from the surface sediments of 67 lakes across a gradient of smelter impacts were examined to determine which measured limnological factors most influenced assemblage composition. Lakes were divided into four categories by acidification histories and location: Acidified (pH < 6) Urban, Non-urban Acidified, Acidified Killarney, and Reference (pH > 6) lakes. Specific conductance, nutrients, alkalinity, and lake depth significantly structured assemblages. Most assemblages were dominated by generalist taxa. Relative abundances of Alona taxa varied strongly along alkalinity and pH gradients. Lakes with high metal concentrations were generally less diverse and dominated by generalist taxa, although metals often co-occurred with other stressors. Recovery targets could be affected by these chemical factors. These data provide useful information for future environmental inferences from cladocerans in metal smelting regions.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| 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".