Data from: Arsenic bioaccumulation in subarctic fishes of a mine-impacted bay on Great Slave Lake, Northwest Territories, Canada
Bibliographic record
Abstract
A subarctic fish community in mine-impacted Yellowknife Bay (Great Slave Lake, Northwest Territories, Canada) was investigated for biological and ecological processes controlling arsenic bioaccumulation. Total concentrations of arsenic, antimony, and metals were measured in over 400 fishes representing 13 species, and primary producers and consumers were included to characterize food web transfer. Yellowknife Bay had slightly more arsenic in surface waters (~3 µg/L) relative to the main body of Great Slave Lake (<1 µg/L), resulting in two-fold higher total arsenic concentrations in muscle of burbot (Lota lota), lake whitefish (Coregonus clupeaformis), and northern pike (Esox lucius). Other mining-associated contaminants, specifically antimony, lead, and silver, were typically below analytical detection in those fish species. No evidence was found for enhanced bioaccumulation of arsenic in long-lived, slow-growing subarctic fishes. Food web biodilution of total arsenic occurred between primary producers, aquatic invertebrates, and fish, although trophic position did not explain arsenic concentrations among fishes. Pelagic-feeding species had higher total arsenic concentrations compared to littoral fishes. Arsenic accumulated in subarctic fishes to comparable levels as fishes from lakes around the world with similar water arsenic concentrations. This first comprehensive study for a subarctic freshwater food web identified the importance of water exposure, biodilution, and habitat-specific feeding on arsenic bioaccumulation.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".