Spatial dependency of soil chemicals in production systems in the anthropogenic dark earth
Bibliographic record
Abstract
In general, anthrosols refer to anthropic soils of high fertility, but the concentration of these nutrients may vary according to the occupation of indigenous people in the past or due to current soil use. This study aimed to evaluate the spatial variability of the chemical attributes of the soil in areas of guandu bean production and pasture and to compare with natural forest systems on anthropogenic dark earth (ADE). For this assessment, 88 sampling points were selected in the area with natural forest vegetation and pasture and 90 sampling points in an area of guandu bean production. Soil samples were collected from layers 0.00–0.05, 0.05–0.10, and 0.10–0.20 m. Chemical analyses of the soil were conducted to determine organic matter, pH, aluminium, soil acidity, phosphorus, potassium, calcium, magnesium, cation-exchange capacity, sum of bases, and base saturation (V%). Data were analyzed using descriptive statistics and geostatistics to sample range, and sample density was estimated for each attribute. Guandu bean showed high content of soil organic matter in relation to pasture in the superficial layer (0.00–0.05 m). Based on sample density, lower variability and higher spatial continuity were observed for guandu bean in relation to pasture and natural forest in the layers of 0.00–0.05 and 0.05–0.10 m. It was found that the use and continuous management of ADE areas alter the content and distribution of soil fertility and, in some cases, may even improve chemical attributes when compared with areas not used with agricultural crops.
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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 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".