Drivers of Circulation Patterns in Colour Lake, Nunavut
Bibliographic record
Abstract
The thermal cycling of lakes is crucial to all aquatic life, greatly influencing primary productivity through the distribution of heat, nutrients, dissolved oxygen and suspended sediments (Liu et al., 2024). The high Arctic region of Canada contains large amounts of lakes covered in ice for up to ten months a year. Logistical and technical difficulties have made year-round monitoring of these lakes difficult, creating a gap in the literature. Quantifying circulation patterns can be done by monitoring conductivity and temperature as they demonstrate how the water is being moved around and stratified. Based on the premise of identifying conductivity and temperature variances in lakes, this study will look to answer how wind, air temperature, solar radiation, and snow depth influence circulation patterns in a representative high Arctic Lake, Colour Lake on Axel Heiberg Island, and what the dominant factors affecting circulation patterns are. The environmental and lake data were collected at fifteen-minute intervals over two years. Wavelet analysis will be used to compare periodicity and phases of the time series data from the lake and environmental variables. Finally, lake surface imagery will then be compared with the results of the wavelet analysis to determine what is going on when the lake is affected by environmental variables and what is going on when it isn’t. Understanding the drivers of lake circulation patterns is critical in our knowledge of northern systems and how climate change may affect them down the road.
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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 | 0.001 |
| 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".