Can remotely sensed catchment to lake area ratios predict mercury levels in subarctic fishes?
Bibliographic record
Abstract
Mercury concentrations ([Hg]) in fish reflect a complex array of interacting biogeochemical and ecological variables. In northern regions where fish are a critical subsistence food, understanding and predicting fish [Hg] can be particularly difficult, largely due to a paucity of comprehensive data associated with the logistical challenges of field sampling. Building on previous work where we elucidated causal relationships between fish [Hg] and a variety of catchment, water quality, and ecological variables in subarctic lakes, we investigated whether using only ratios of catchment area to lake area (CA:LA) can predict [Hg] in northern freshwater fish species. As CA:LA can be sensed remotely, they may be more feasible and practical to obtain than field data in far northern regions. Our study included thirteen remote lakes that represent a CA:LA gradient of 6.2–423.5 within an ∼66,000 km2 subarctic region of Northwest Territories, Canada. We found that size-standardized [Hg] in three widespread fish species, including Lake Whitefish (Coregonus clupeaformis), Walleye (Sander vitreus), and Northern Pike (Esox lucius), were significantly and positively related to CA:LA (p < 0.007, r2 = 67–80%), indicating higher fish [Hg] in smaller lakes surrounded by relatively larger catchments. Our findings provide compelling evidence that remotely sensed CA:LA can be used to predict [Hg] in northern fishes and aid in prioritizing understudied and subsistence fishing lakes of the Canadian subarctic for [Hg] monitoring programs.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".