Quality indicators on plinthosols and its relations with maize productivity in alley cropping system
Bibliographic record
Abstract
The slash and burn is practiced in Maranhao State, Brazil, which soils are affected by adverse climate conditions, as continuous cycles of rigorous rain and drought, with many problems of impeditive layers, superficial crust and, consequently, bad draining, affecting crops. In that conditions it was implanted on rural settlement area, in 2002, an alternative system, alley cropping with Clitoria fairchildiana, an leguminous. In that system, annually the leguminous are pruned, and its branches put on soil to maintain the covering of surface. In 2005, the maize was planted in no-tillage system. The aim of this study was to determine the chemical and physical quality indicators of the Plinthosols. The area of experiment was marked, using 44 lines of leguminous, forming grids of 10x10m, beginning in the center of leguminous, in joint the maize rows. All the grid points were geoferrered in cartesian plan, performing 113 points. The sample was “square grid” mode. The chemical indicators were phosphorus, potassium, calcium, magnesium, potential acidity, organic carbon and pH in KCl and the physical were bulk density, total porosity, aeration capacity and water superficial permeation capacity by Guelph permeameter method. Preliminarly the indicators were analysed by descriptive statistic and after, by geostatistic. Chemical indicators were more relevant to affect the maize productivity than physical indicators. Among the soil physical indicators the superficial permeation was the more influenced maize productivity. Although the bulk density was high, (higher than 1,4g.cm-3), didn’t affect that much the maize productivity. Geostatistic revealed as an useful instrument, better than descriptive statistic, to show indicators variability and to show better form in case to adopt new practice of cultivation.
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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 | 0.001 |
| 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) | 0.001 | 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".