Historical gold mining increased metal(loid) concentrations in lake sediments from Nova Scotia, Canada
Bibliographic record
Abstract
Historical gold mining operations between the 1860s and 1940s have left substantial quantities of arsenic- and mercury-rich tailings near abandoned mines in remote and urban areas of Nova Scotia, Canada. Large amounts of materials from the tailings have entered the surface waters of downstream aquatic ecosystems at concentrations that present a risk to benthos. We used paleolimnological approaches to examine long-term trends in sedimentary metal(loid) concentrations, assess potential sediment toxicity, and determine if geochemical recovery has occurred at four lakes located downstream of three productive gold-mining districts. During the historical mining era, sedimentary total arsenic and mercury concentrations and enrichment factors increased substantially at all downstream lakes that received inputs from tailings. Similarly, chromium, lead, and zinc concentrations increased in the sediments after mining activities began and the urbanization that followed. The calculated probable effects of concentration quotients (PEC-Qs) for sediments exceeded the probable biological effects threshold (PEC-Q > 2) during the mining era. Although sedimentary metal(loid) concentrations have decreased for most elements in recent sediments, relatively higher PEC-Q and continued exceedance of Canadian Interim Sediment Quality Guidelines suggest that complete geochemical recovery has not occurred. It is likely that surface runoff from tailing fields, urbanization, and climate-mediated changes are impacting geochemical recovery trajectories.
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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 | 0.002 |
| 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".