Mercury Trends in Predatory Fish in Great Slave Lake: The Influence of Temperature and Other Climate Drivers
Bibliographic record
Abstract
Here we report on trends in mercury (Hg) concentrations in lake trout (Salvelinus namaycush), burbot (Lota lota), and northern pike (Esox lucius) from Great Slave Lake, located in the Mackenzie River Basin (MRB) and investigate how climate factors may be influencing these trends. Hg concentrations in lake trout and burbot increased significantly over the early 1990s to 2012 in the two major regions of the lake; no trend was evident for northern pike over 1999-2012. Temporal variations in Hg concentrations in lake trout and burbot were similar with respect to timing of peaks and troughs. Inclusion of climate variables based on annual means, particularly temperature, improved explanatory power for variations in Hg over analyses based only on year and fish length; unexpectedly, the temperature coefficient was negative. Climate analyses based on growing season means (defined as May-September) had less explanatory power suggesting that trends were more strongly associated with colder months within the year. Inclusion of the Pacific/North American index improved explanatory power for the lake trout model suggesting that trends may have been affected by air circulation patterns. Overall, while our study confirmed previously reported trends of Hg increase in burbot in the MRB, we found no evidence that these trends were directly driven by increasing temperatures and productivity.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".