Mapping of Central Pivot Irrigation in the Hydrographic Basin of the Goiano Tributaries of the Araguaia River
Bibliographic record
Abstract
The use of irrigation has expanded and favored agricultural productivity in recent years. The mapping through remote sensing has contributed to the monitoring of irrigated areas. In this sense, the objective of this study was to evaluate the central pivotal evolution in terms of location by municipalities, micro basins, soils and slope in the Goian tributary watershed of the Araguaia River State of Goiás. Data were available between 2000 and 2016. Irrigated areas were surveyed through the database available in the Geographical Information System of the State of Goiás (SGEI). The vector and raster data were manipulated using the Qgis v software. 2.18.26 (QGIS Development, 2019). The pivots were counted through the statistical function of the software. From the shape SGEI available in the soil map of classes is generated by categorizes tion of soil types. The declivity map was generated from raster files acquired through the Brazilian Geo morphological Database (INPE, 2017). The slope classes (%) were extracted with slope tool. There is an increase of more than 95% in the number of pivots and irrigated area between the years 2000 and 2016. The central pivots are more concentrated in the central region of the Red and Red-Light basins. The highest concentration of central pivots occurred in the municipality of Jussara. The pivots are located predominantly in an Oxisol area with a slope of 3 to 13%.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".