Calibration Methods for Estimation of Reference Evapotranspiration in Morro Do Chapéu, Bahia, Brazil
Bibliographic record
Abstract
The objective of this work was to calibrate and to validate the methods of Camargo, Hargreaves and Samani, and Priestley and Taylor, according to the Penman-Monteith model, for the estimation of reference evapotranspiration (ETo), in the four seasons of the year for the municipality of Morro do Chapéu, Bahia. Climatological data from the conventional meteorological station belonging to the National Institute of Meteorology (INMET) were used, in the period of 18 years (2000-2018). The first 16 years were considered to adjust the parameters. The years of 2016 and 2017 were assigned as independent data to validate the adjustments. To analyze the results, it was used the root mean square error (RMSE in mm d-1), coefficient of determination (R2), systematic error (BIAS in mm d-1), and Willmott’s concordance index. After adjusting the parameters, the three methods improved their performance in the estimation of ETo, however, the Camargo method presented high values of RMSE, reaching 0.41 mm d-1 during the spring. It is concluded that the calibrated methods of Hargreaves and Samani, and, Priestley and Taylor can be recommended for the estimation of the reference evapotranspiration, for planning and execution of irrigation projects in the municipality, regardless of the season.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".