An analysis of ice growth and temperature dynamics in two Canadian subarctic lakes
Bibliographic record
Abstract
The seasonal dynamics of freshwater lake ice and its interactions with air and snow are studied in two small subarctic lakes with comparable surface areas but contrasting depths (4.3 versus 91 m). Two, 2.9 m long thermistor chain sensors (Snow and Ice Mass Balance Apparatuses), were used to remotely measure air, snow, ice, and water temperatures every 15-min between December 2021 and March 2022. Results showed that freeze-up occurred later in the deeper lake (Ryan Lake) and earlier in the shallow lake (Landing Lake). Ice growth was significantly faster in Ryan Lake than in Landing Lake, due to cold water temperatures (mean (Tw¯) =0.65 to 0.96°C) persisting beneath the ice. In Landing Lake, basal ice growth was hindered because of warm water temperatures (Tw¯=1.5 to 2.1°C) caused by heat released from lake sediments. Variability in air temperatures at both lakes had significant influences on the thermal regimes of ice and snow, particularly in Ryan Lake, where ice temperatures were more sensitive to rapid changes in air temperatures. This finding suggests that conductive heat transfer through the air-water continuum may be more sensitive to variability in air temperatures in deeper lakes with colder water temperatures than in shallow lakes with warmer water temperatures, if snow depths and densities are comparable. This study highlights the significance of lake morphology and rapid air temperature variability on influencing ice growth processes. Conclusions drawn aim to improve the representation of ice growth processes in regional and global climate models, and to improve ice safety for northern communities.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.009 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 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 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".