Using GIS towards the Characterization and Soil Mapping of the Caia Irrigation Perimeter
Bibliographic record
Abstract
The Caia Irrigation Perimeter is an irrigation infrastructure implemented in 1968. As is often the case, the original soil map of this region (dated from 1961) does not have the detail needed to characterize a relatively small-sized zone, where intensive agricultural practices take place. Using FAO methodology and with the main goal of establishing a larger-scale soil map, adequate for the demands of a modern and intensive agriculture, we gathered the geological characterization of the study area and information about the topography, climate, and vegetation of the region. Using ArcGIS software, we overlapped this information and established a pre-map of soil resources. Based on this pre-map, we defined a set of detailed itineraries in the field, evenly distributed, in which soil samples were collected. In those distinct soil units, we opened several soil profiles, from which we selected 26 to analyze in the present study, since they characterized the existing diversity in terms of soil type and soil properties. Based on the work of verification, correction, and reinterpretation of the preliminary soil map, we reached a final soil map for the Caia Irrigation Perimeter, which is characterized by enormous heterogeneity, typical of Mediterranean soils, containing 23 distinct cartographic units, the most representative being the Distric Fluvisols with inclusions of Luvisols Distric occupying 29.9% of the total study area, and Calcisols Luvic with inclusions of Luvisols endoleptic with 11.9% of the total area. Considering the obtained information on soil properties; ArcGIS was used to develop a map in which it was possible to ascertain the impact of the continuous practice of irrigation in this area. This allows us to put forward relevant conclusions on the need to access and monitor specific Mediterranean soils in order to mitigate the environmental impact of irrigation practices.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".