Environmental variables associated with littoral macroinvertebrate community composition in Arctic lakes
Bibliographic record
Abstract
The relationship between littoral macroinvertebrate communities and environmental gradients in Arctic lakes is poorly understood, making it difficult to predict whether these important components of lake ecosystems will be affected by emerging stressors such as permafrost thaw and road development. To better understand how littoral macroinvertebrates are related to environmental gradients, we characterized macroinvertebrate communities and environmental variables for 32 Arctic lakes across the boreal–tundra transition in the Northwest Territories. Our analysis showed that a small selection of variables had strong relationships with community structure: calcium, conductivity, latitude, surface area, catchment area, percent fine sediment, chlorophyll a, and whitefish (Coregonus clupeaformis or Coregonus nasus) presence. Many of these variables, including calcium, conductivity, and chlorophyll a levels, are affected by permafrost thaw and road dust contamination. Based on the direction and magnitude of these relationships, we hypothesize that macroinvertebrate abundance might decline in response to permafrost thaw and road dust contamination, while taxon diversity may rise. While correlative in nature, our results and hypotheses may be valuable as future studies evaluate ongoing changes in Canada’s Arctic lakes.
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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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".