Identification of Areas Irrigated by Central Pivot in the State of Goiás, Brazil
Bibliographic record
Abstract
The state of Goiás, Brazil offers a territorial division with well-defined areas in terms of water capacity. The water found in these dividers is used in various agriculture segments. There were identified in the Hydrographic Basins in the State, areas irrigated by central pivots used in irrigated agriculture that is developing very fast and this can become a problem if a control is not done. This study aimed to collect data, identify and quantify the distribution of irrigation systems of the central pivot type licensed and operating in a spatial format in the Hydrographic Basins in the State of Goiás. It raised digital data and physical media in order to understand how these were able to characterize the research area. It analyzed the maps, existing in the database of the State through digital (SIEG-State System of Statistics and Geographic Information of Goiás). With the use of AutoCAD programs, version 2018, there was utilized the geographic information plataform QGIS 2.14.19 with GRASS 7.2.1 has organized thematic maps of hydrographs and pivots. This material provided the possibility of compiling the fundamental data to structure the information that supports the descriptive dynamics of the number of pivots even in separate basins. This information analyzed and compared to other publications about pivots in Goiás contributed to the formation and elaboration of a data model for the year 2017. In the State of Goiás, Brazil, there is a total of 3,223 central pivot type equipment in operation irrigating an area corresponding to 234,226.12 hectares.
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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 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 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".