Long-term changes in Wolf Lake (Sudbury region, Ontario) situated in Mi'iangan Zaagagan Preserve: A proposed Indigenous conservation area
Bibliographic record
Abstract
Over a century of large-scale mining and smelting in Sudbury (Ontario, Canada) has impacted nearby aquatic ecosystems. While considerable research exists on Sudbury-area lakes, few studies focus on lands important to Indigenous communities. Wolf Lake, located ∼50 km northeast of Sudbury, acidified in the 1960s from long-range acidic deposition from regional smelters. While emission reductions ca. 1970s have been tracked through water chemistry monitoring, baseline conditions remain unknown. Using indicators preserved in lake sediments, we examined long-term effects of acidic emissions and other stressors on Wolf Lake’s aquatic biota. After peak atmospheric emissions, reconstructed lake water pH dropped to ∼5.1, with concurrent decreases in sediment chlorophyll a (chl a) and dissolved organic carbon (DOC). These changes coincided with elevated sediment metal levels. Recent sediments show partial recovery, with pH, DOC, and chl a returning towards pre-disturbance levels. However, sediment metal levels remain high, and diatom assemblages have not fully recovered. Understanding these changes offers management insights for the Wahnapitae First Nation’s proposal for an other effective area-based conservation measure (OECM) designation for Wolf Lake.
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.
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 | 0.000 | 0.001 |
| 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".