Investigating Climate Characteristics And Their Influence On Heat Flow Through Temperate Region Lake Ice
Bibliographic record
Abstract
This study uses in situ data to determine the heat flow through temperate region lake ice from 2017 to 2022. The study sites, MacDonald Lake and Clear Lake are located in Central Ontario and represent typical lakes in the region. Climate and ice observations were recorded weekly, while ice decay/growth was determined using a Shallow Water Ice Profiler and manual measurements. Results found that there was an increase in slushing events from 2018 to 2022. When slush was present on ice, the temperature gradient through the ice was nonexistent – resulting in an isothermal ice column and no heat flow. Ice thickness and temperatures at the ice-atmosphere interface were the main drivers of heat loss, most notably a maximum heat loss of 0.5 Wm-2, showing less heat loss than through Arctic ice. Finally, there was local-scale variation throughout climate and ice observations, driven by differing site conditions and snow redistribution.
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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 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".