The Cooling and Heating Impacts of a Lake and a Nearby Wetland Under Current and Changing Climate
Bibliographic record
Abstract
Understanding the effect of different landscapes on local temperature is important for understanding land-atmospheric interactions, and is a critical step toward an informed urban design under a changing climate. This presentation considers Lake Memphremagog, a transboundary water body between Quebec and Vermont, along with one of its adjacent wetlands as a test bed to study the impact of different water bodies on regulating the local temperature. We use the data from two identical climate stations to study hourly temperature, absolute and relative humidity as well as incoming and outgoing radiation components along with vapour pressure deficit at the lake and wetland. We benchmark the temperature measurements in these two sites with the data gauged in an Environment and Climate Change Canada’s weather station located between the two sites. Using a systematic analysis, we account for the cooling and heating impacts of the lake and the wetland and demonstrate their underlying causes. We show that during the growing season and at the daily scale, the cooling impacts of the wetland can cancel out the heating impacts of the lake. This is not the case during day times, in which the lake acts as a sink of heat, while the wetland is the source. We show that the cooling and heating effects of the considered lake-wetland duo can be described by the daily temperature statistics (i.e. average, minimum, maximum and range) at the benchmark weather station. This provides an opportunity to create stochastic models for retrospective and prospective projections of cooling and heating impacts of this lake-wetland duo under current and future climate.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".