Young gravel-pit lakes along Canada’s Dempster Highway: How do they compare with natural lakes?
Bibliographic record
Abstract
Gravel-pit lakes are a common feature of many human-modified landscapes throughout the world. In Canada’s north, they are often formed when gravel is extracted to construct dams, bridges, and highways. Past studies suggest that gravel-pit lakes differ from natural lakes in terms of their morphometry, water quality, and biological communities. In this study, we compared gravel-pit and natural lakes by sampling lakes between Inuvik and Fort McPherson in the Northwest Territories. We collected lake morphometry, water quality, and biological data (zooplankton, macroinvertebrates, and fish presence) from six gravel-pit lakes and fifteen natural lakes. In comparison to natural lakes, gravel-pit lakes were four times deeper, two times clearer, and five times smaller in their surface area. In addition, important nutrients, including phosphorus and nitrogen, were significantly lower in gravel-pit lakes. Despite the differences in morphometry and nutrients, pelagic zooplankton and littoral macroinvertebrate communities did not differ significantly between the two lake types. Therefore, we conclude that despite their recent formation and unnatural morphometry, gravel-pit lakes along the Dempster Highway can support invertebrate communities typical of natural lakes in the region.
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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 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".