Impacts of asbestos mining activities on lake ecosystems: insights from a multi-proxy paleolimnological investigation
Bibliographic record
Abstract
The impacts of asbestos mining activities and wastes on aquatic ecosystems are generally assumed to be minimal, yet have been poorly studied. To evaluate their importance, we analyzed several sediment cores collected in the Bécancour River Basin, notably in 4 fluvial lakes located downstream from Thetford Mines (Quebec, Canada): Stater Pond, Trout Lake, Lake William, and Lake Joseph. This region has been the center of more than a century of asbestos mining activities (1877–2011 CE), which resulted in the accumulation of huge piles of wastes (tailings and waste rock) on riverbanks. Age-depth models, primarily derived from radiometric dating (137Cs, 210Pb, 14C), revealed extreme increases in sediment accumulation rates in Stater Pond and lakes Trout and William, corresponding with the 1955–1959 CE drainage and excavation of an upstream lake for mining purposes. This event also corresponded with their strong eutrophication, as revealed by sudden changes in diatom assemblage composition (e.g., proliferation of Cyclostephanos invisitatus/makarovae, Cyclotella meneghiniana). ICP-MS/ICP-AES analyses revealed that post-1960 sediments at Stater Pond and lakes Trout and William, which maintained very high accumulation rates, were distinctively enriched in magnesium, chromium, and nickel. This provided evidence that they are contaminated by asbestos mining wastes, hence that the piles on the riverbanks are exposed to heavy erosion. Analyses by transmission electron microscopy demonstrated that post-1960 sediments contain important asbestos fiber concentrations (up to 6.9 wt%). Evidence of asbestos mining contamination has also been found in Lake Joseph, which showed that it spreads over ≥ 25 km in the Bécancour River system. We conclude that asbestos mining activities and wastes are susceptible to cause high sediment loads, as well as metal and fiber contamination in aquatic ecosystems (among other undesirable effects). Therefore, more efforts should be invested in the restoration of such mining sites and in controlling pollution they cause.
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".