The impacts of century-old, arsenic-rich, mine tailings on multitrophic level biological assemblages in lakes from the Cobalt, Ontario, Canada region
Bibliographic record
Abstract
Silver mining in the early 1900s has left a legacy of arsenic-rich mine tailings around the town of Cobalt, in northeastern Ontario, Canada.Due to a lack of environmental control and regulations at that time, it was common for mines to dump their waste into adjacent lakes and land depressions, concentrating metals and metalloids in sensitive aquatic ecosystems.In order to examine what impacts, if any, these century-old, arsenic-rich, mine tailings are having on present day aquatic ecosystems we sampled diatom assemblages in lake surface sediment in 24 lakes along a gradient of surface water arsenic contamination (0.4 -972 µg/L).In addition, we examined sedimentary cladocera and chironomid abundances and community composition, as well as open water zooplankton, and chlorophyll-a concentrations over 10 of these study lakes along a gradient of arsenic contamination (0.9 -1,113 µg/L).Our results show that present-day arsenic concentration is not a significant driver of biotic community change across the study lakes, suggesting that other variables such as lake depth and pH are more important in structuring the biological community across these lakes.These results suggest that while legacy contamination has greatly increased metal concentration beyond Canadian Council of Ministers of the Environment's (CCME) guideline for aquatic life (5 µg/L), variability in lake morphometry and water chemistry among the study lakes appears more important in the structuring of aquatic ecosystems in Cobalt, Ontario, Canada.
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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 | 0.002 | 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".