Lake Evaporation and Evaluation of Seven Estimation Methods from Dickie Lake, South-Central Ontario, Canada: A Long Term Study
Bibliographic record
Abstract
For understanding lake evaporation, developing estimation techniques, and assessing the effects of evaporation change (caused by climate change or land use change) on water resources, long-term observations and field data are crucial. For the study and management of water resources and ecosystems, it has been essential to establish accurate calculation methods for lake evaporation. To the few long-term studies on lake evaporation, a 30-year dataset from Dickie Lake in south-central Ontario, Canada, was added. Based on field meteorology, hydrology, and lake water temperature data, seven evaporation methods were used to separately calculate evaporation during the ice-free season. Using a lake energy budget model, the actual evaporation measured over a period of a year was estimated, and the estimation served as the reference evaporation for the evaluation of the seven methods. A performance ranking based on the root mean squared deviation and coefficient of efficiency was proposed after comparing the deviation of the seven methods' induced evaporation from the reference evaporation. The present study results have shown a similar energy budget pattern as other studies in similar climatic regions, and identified a performance rank for the evaporation calculation methods to be used for lakes in Canadian Shield.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".