Models for Estimating Reference Evapotranspiration in Different Periods in Rio Verde, Goiás, Brazil
Bibliographic record
Abstract
The water management in irrigated agriculture begins determining the need of water for the culture. Therefore, it was intended to evaluate the performance of the models of estimation reference of evapotranspiration (ETo) with regard to the method Penman-Monteith (PM), standard method, for Brazilian Cerrado Region (tropical grassland/savannah). The climate elements were obtained from the conventional weather station of Rio Verde from January/1972 to December/2016. It was compared the performance of the daily average ETo, during the dry, rainy and annual periods, by the PM method with regard to another 26 methods. Through the coefficient of determination, it was verified the methods of Turc (T) and Radiation-Temperature (RT) approached more to the PM, at any time of the year, being able to replace the standard method. The ETo average in the annual period was 3.8 mm day-1, for the dry period due to the smallest amount of solar radiation, the period submitted lower levels of ETo. The other models in which were used fewer amounts of climate data, they overestimated or underestimated the PM model by up to 57.9% and 60.7% respectively. With the management of water in agriculture, water availability can be increased in the hydric bodies, characterizing it as a tool for water management with the rational use of water resources.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".