A century of tailings migration from silver mining reduced biodiversity in a Boreal Shield lake
Bibliographic record
Abstract
The "Cobalt silver rush" (Cobalt, Ontario, Canada), which occurred near the early-20th century, led to the establishment of several silver mines in the region, resulting in the surrounding ecosystems becoming contaminated with mine wastes disposed of directly into lake basins. Ibsen Pond, however, was not used to store tailings. Yet, the elevated levels of metal(loid)s in the surface waters indicated that mine wastes may have entered this lake via a connecting stream. Here, we used a paleoecotoxicological approach to investigate: 1) when migration of mine tailings into this lake began; and 2) how it altered aquatic biodiversity across multiple trophic levels. The geochemical proxies tracked the pollution history of Ibsen Pond, with sharp decreases in sedimentary organic content and concomitant increases in the concentrations of several toxic metal(loid)s between ca. 1910 and 1940, matching the time when regional silver mining activities peaked. The calculated probable effects concentration quotients (PEC-Q) for sediments deposited after mining contaminants entered the lake exceeded the probable biological effects threshold (PEC-Q > 2). Examination of sedimentary diatom (Bacillariophyceae) and cladoceran (Branchiopoda) assemblages revealed notable decreases in the relative abundances of several littoral taxa, along with declines in Hill's N2 diversity across both bioindicator groups. Our paleoecotoxicological analyses show that migration of mine wastes via stream inflow can be an important source of pollution to previously uncontaminated lakes near abandoned mines and ultimately cause substantial biological damage.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 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".