Long-term changes in Wolf Lake (Sudbury region, Ontario) situated in Mi’iangan Zaagagan Preserve: a proposed Indigenous conservation area
Bibliographic record
Abstract
Aquatic ecosystems in northern Ontario (Canada) have been widely studied for responses to anthropogenic activities. Specifically, the adverse effects from metal smelting has cause atmospheric deposition of acidic and metal pollution. Sudbury (Ontario) has been considerable researched for the effects of century long large-scale mining and smelting operations; however, local Indigenous freshwater resources have been largely ignored. Wolf Lake, located ~50 km northeast of Sudbury, is a culturally important site to the Wahnapitae First Nation and local community, providing many ecosystem services. Given its significance, the lake and surrounding old growth red pine (Pinus resinosa) forest is being proposed as an Other Effective area-based Conservation Measure. To determine pre-disturbance conditions to inform management decisions for the proposed conservation site, paleo-ecotoxicological approaches were applied. Here we use a multi-proxy approach of water chemistry records and sedimentary metals, chlorophyll a, dissolved organic carbon, and diatom assemblages from a dated sediment core to reconstruct long-term changes in Wolf Lake. A 40-year water chemistry dataset was analyzed from the Ministry of Environment, Conservation and Parks to reconstruct long-term lake water metal concentrations against sedimentary metal concentrations. Sedimentary metal concentrations were used to plot against threshold and probable effect concentrations and calculate enrichment factors. Chlorophyll a values were used to infer paleoproduction while dissolved organic carbon was used to infer past lake water conditions. The diatom assemblage was applied to a regional transfer function to estimate past lake water pH. Results indicated that Wolf Lake experienced lake water acidification and metal pollution linked to atmospheric deposition and complete recovery has yet to occur.
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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 | 0.000 |
| 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".