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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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 teacher head, 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".