Before and after mink fur farming: water chemistry and sedimentary diatom assemblages in lakes from southwestern Nova Scotia, Canada
Bibliographic record
Abstract
Libera N, Rühland KM, Kurek J, Smol JP. 2024. Before and after mink fur farming: water chemistry and sedimentary diatom assemblages in lakes from southwestern Nova Scotia, Canada. Lake Reserv Manage. 40:18–35.Since the 1930s, farmed mink pelts have been an important contributor to the economy of rural Nova Scotia (NS, Canada). However, these farms are a potential source of pollutants to nearby ecosystems. To determine how regional lakes have been affected by fur farms, we compared modern water chemistry and sedimentary diatom assemblages in 14 lakes in southwestern Nova Scotia. We categorized lakes into 3 groups: (a) 5 lakes with fur farms in the catchment or near inlets; (b) 4 lakes downstream from or near fur farms; and (c) 5 reference lakes without hydrological connection to fur farms. To assess whether lake conditions have changed since the establishment of fur farms, we conducted a “before-and-after” paleolimnological analysis comparing surface sediments (representing modern environments) to dated sediments deposited prior to fur farm operations. Diatom assemblages registered distinct responses to eutrophication in several hypereutrophic lakes with farms within their catchment boundaries. In lakes ∼25 km downstream from fur farms, diatom assemblage changes were more characteristic of climate warming and acidification than of eutrophication, despite surface water nutrient concentrations well above levels in reference lakes. We found no association between the presence of fur farms and surface water trace metals that could potentially bioaccumulate and/or biomagnify. Our data indicate that decades of nutrient inputs from fur farms have caused eutrophication at several lakes where fur farms are close to the shoreline or to inlet streams.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".