Leeches in acidified lakes of central Ontario, Canada: Status and trends
Bibliographic record
Abstract
Lakes in the acid-sensitive regions of Sudbury, Algoma, and Muskoka, Ontario (Canada), were examined to assess relationships between leech populations and certain chemical and physical characteristics of the lakes (pH, conductivity, total nitrogen, dissolved organic carbon, and water depth). Thirteen leech species were trapped, and leeches occurred in 81% of study lakes. Leech species richness was higher in lakes with high pH (i.e., less acid) and low conductivity. Occurrence and abundance of some species were significantly increased in lakes with higher pH and lower conductivity; however, abundance models explained low portions of data variability (10-13%). Temporal trends of leech occurrence, species richness, and abundance in the Sudbury study area were examined in four separate years over a nine-year interval. Most lakes had no significant change in leech richness or abundance over this period. However, a substantial subset of the lakes showed declines in richness or abundance despite dramatic reductions in acidic deposition across eastern North America and some subsequent improvements in lake chemistries. Our results suggest that leech declines were not directly related to changes in lake chemistry. Hence, we suggest that leeches are not suitable as direct indicators of chemical recovery from acidification.
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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 | 0.002 |
| Science and technology studies | 0.001 | 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".