Evidence of eutrophication in Arctic lakes
Bibliographic record
Abstract
Lakes and ponds are dominant components of Arctic landscapes and provide food and water for northern communities. In the Greiner Lake watershed, in Cambridge Bay (Nunavut, Canada), water bodies are small (84% <5 ha) and shallow (99% <4 m deep). Such characteristics make them vulnerable to eutrophication as temperatures rise and nutrient concentrations from the greening landscape increase. Here, we investigated and compared 35 lakes and ponds in the Greiner watershed in August 2018 and 2019 to determine their current trophic states based on their chemical composition and phytoplankton communities. The ponds had higher trophic status than the lakes, but overall, most sites were oligotrophic. Lake ERA5, located upstream of any direct human influence was classified as eutrophic due to high total phosphorus (32.3 μg·L−1) and a high proportion of Cyanobacteria (42.9% of total phytoplankton biovolume). Satellite imagery suggests the lake may have been eutrophic for the last 30 years. We hypothesize that the coupled effects of catchment characteristics and elevated local snow accumulation patterns promote higher nutrient leaching rates from the soils. We recommend further analysis and monitoring as eutrophication could become more widespread with ongoing climate change and the associated increases in temperature, precipitation, and catchment–lake coupling.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".