High‐Frequency Observations of Temperature and Dissolved Oxygen Reveal Under‐Ice Convection in a Large Lake
Bibliographic record
Abstract
Abstract Detailed observations of thermal structure over an entire winter in a large lake reveal the presence of large (10–20 m) overturns under the ice, driven by diurnal solar heating. Convection can occur in the early winter, but the most vigorous convection occurred near the end of winter. Both periods are when our lake ice model suggest thinner ice that would have been transparent. This under‐ice convection led to a deepening of the mixed layer over time, consistent with previous short‐term studies. During periods of vigorous convection under the ice at the end of winter, the dissolved oxygen had become supersaturated from the surface to 23 m below the surface, suggesting abundant algal growth. Analysis of our high‐frequency observations over the entire winter of 2015 using the Thorpe‐scale method quantified the scale of mixing. Furthermore, it revealed that changes in oxygen concentrations are closely related to the intensity of mixing.
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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 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| 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".