Scaled Semivariogram in the Sample Planning of Soils Cultivated With Sugarcane
Bibliographic record
Abstract
Sugarcane cultivation has expanded in areas previously occupied by degraded pastures. In the first years of cultivation, besides the physical and chemical restrictions of the soils, other factors can make impossible the maximum productive expression of the crop, like the climatic and edaphic factors. The objective of this work was to evaluate the ideal sampling density and spatial variability of the physical and chemical attributes of soils cultivated with sugarcane. Georeferenced data provided by the Sugarcane Technology Center (STC) of an area of approximately 19,000 hectares located in the northwest region of São Paulo were evaluated. The granulometric fractions of the soils and organic matter contents and base saturation were determined at depths of 0.00-0.25 and 0.25-0.50 m. An index named edaphic environment (ENV) was calculated based on the records of rainfall of the areas and the productivity of the sugarcane, being represented with aptitude scores ranging from 0 (worst condition) to 10 (best condition). The results showed a strong correlation between clay and organic matter attributes with ENV index. Regions with aptitude ≥ 6.65 of ENV index corresponded to sites with clay (CL) and organic matter (OM) content above 335 g kg-1 and 30 g kg-3, respectively. Only 10.86% of the area presented base saturation (V%) concentration ≥ 68%, correlating positively with CL and ENV. Through the scaled semivariogram it was possible to verify that a density of sampling of a sample to each 18 ha can be used for a mapping in macroscale of the evaluated attributes in the northwest region of the state of São Paulo.
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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 | 0.005 |
| 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 | 0.000 | 0.000 |
| 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".