Leeches in acidified lakes of central Ontario, Canada: Status and trends
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.000 | 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 it