Determination of Evaporative Fluxes Using a Bench-Scale Atmosphere Simulator
Bibliographic record
Abstract
An accurate determination of evaporative fluxes is critical for efficient water management in semi-arid climates such as in the Canadian Prairies. The main achievements of this research are the design and operation of a bench-scale atmosphere simulator, performance evaluation using selected weather scenarios pertaining to regional atmospheric conditions, validation using established empirical correlations, and estimation of evaporation rates and the amount for a typical local water body. Results indicate that the measured data achieved the target values for the various parameters and the data were found to be stable during the 3-h test duration. The vapour flux was found to have large variation during summer (0.120 g∙s−1∙m−2 during the day and 0.047 g∙s−1∙m−2 at night), low variation during spring (0.116 g∙s−1∙m−2 during the day and 0.062 g∙s−1∙m−2 at night), and negligible change during fall (0.100 g∙s−1∙m−2 during the day and 0.076 g∙s−1∙m−2 at night). The measured vapour flux was generally within one standard deviation of the equality line when compared with that predicted by both the mass-transfer equations and the combination equations. The average evaporation ranged from 4 mm∙d−1 to 8 mm∙d−1 during the day and decreased to 1 mm∙d−1 to 3 mm∙d−1 at night. The 24-h evaporation was found to be 8 ± 1 mm∙d−1 from late April through late October. Likewise, the cumulative annual evaporation was found to be 1781 mm, of which 82% occurs during the day and 18% at night.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".