A paleolimnological archive of metal sequestration and release in the Cumberland Basin Marshes, Atlantic Canada
Bibliographic record
Abstract
We used a paleolimnological approach at Long Lake, Nova Scotia, to construct a 10 500-year record of metal deposition in lakebed sediments and elucidate the influence of both natural and anthropogenic environmental changes. Aquatic sediment concentrations of mercury (Hg), arsenic (As), and chromium (Cr) in Long Lake fluctuated substantially and, during some periods, exceeded guidelines for the protection of aquatic life. Increases in lead (Pb), Hg, Cr, trace metals, and nitrogen stable isotopes (δ15N) were broadly coincident with a period of widespread drying from ca. 8000 to 4000 cal BP and were likely a consequence of regional fires. From ca. 4000 cal BP until 1700 AD, metal levels in general were low due to decreased erosion, increased precipitation, and reduced fire activity. Water level lowering and forced sediment aggradation (tiding) in the 1800s led to increases in minerogenic Pb and Cr, though fossil fuel combustion also likely contributed to total Pb concentrations. Stratigraphic proxies indicated increased inorganic sedimentation rates, and reduced autochthonous productivity were coincident with lower Hg and As concentrations in the Long Lake sediment. Our data indicate that natural phenomena (fire) can result in sediment contaminant exceedances, that most metals have multiple sources, and that both human-induced disturbance and emissions have contributed to Pb contamination in the last 200 years. In addition, wetter and generally cooler climate appeared to favour lower concentrations of contaminants in lake sediments. Although wetland sediments in the Cumberland Basin Marshes are not heavily polluted with metals, the development of constructed wetlands and the disruption of aquatic sediments have the potential to concentrate, mobilize, and increase the bioavailability of metals.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".