Evaluation of Precipitation and Evapotranspiration Obtained by Remote Sensing With Meteorological Stations in the State of Goiás
Bibliographic record
Abstract
The measures of precipitation and evapotranspiration can be realized by means of meteorological predictors or by meteorological satellites. Both types have advantages and disadvantages. Emission is one of the most important climatic parameters for a region’s socioeconomic and environmental formation, while evapotranspiration is more important for global and local climate mediators. The objective of this work was to analyze the comparative form of the rainfall satellite data through the TRMM and evapotranspiration satellite, by the MODIS satellite, with surface data, that is, of meteorological demarcations, distributed in the State of Goiás, in the years of 2012 and 2013. The monthly measurements were taken in meteorological stations (automatic and conventional), distributed and representative in the State of Goiás. Data collection of the stations was done through the INMET website. The data of the TRMM satellite were obtained from the LAPIG-MAPS platform, developed by the Laboratory of Image Processing and Geoprocessing of the Federal University of Goiás (LAPIG/UFG), for the generation of the quarterly maps for the year 2012 and 2013. The high regression between the data of the surface meteorological stations and the data of TRMM satellite for the year of 2012 and 2013 allows to affirm a high reliability to the orbital data. The evapotranspiration data present low correlation between satellite data (MOD16) and surface stations. Still, this information evidences high potentiality and availability of information in large spatial and temporal scale.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".