Evaluation and generalization of temperature‐based methods for calculating evaporation
Bibliographic record
Abstract
Abstract Seven temperature‐based equations, each representing a typical form, were evaluated and compared for determining evaporation at two climatological stations (Rawson Lake and Atikokan) in north‐western Ontario, Canada. The comparison was first made using the original constant values involved in each equation, and then using the recalibrated constant values. The results show that when the original constant values were used, larger biases existed for most of the equations for both stations. When recalibrated constant values were substituted for the original constant values, six of the seven equations improved for both stations. Using locally calibrated parameter values, all seven equations worked well for determining mean seasonal evaporation values. For monthly evaporation values, the modified Blaney–Criddle method produced least error for all months for both stations, followed by the Hargreaves and Thornthwaite methods. The Linacre, Kharrufa and Hamon methods showed a significant bias in September for both stations. With properly determined constant values, the modified Blaney–Criddle, the Hargreaves and Thornthwaite methods can be recommended for estimating evaporation in the study region, as far as temperature‐based methods are concerned. Copyright © 2001 John Wiley & Sons, Ltd.
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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 | 0.010 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 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".