Structuring of a Haplortox by Soil Cover Species
Bibliographic record
Abstract
The objective of this work was to evaluate the effect of soil cover species and management systems in improving the physical characteristics of a Haplortox and its effects on grain yield and soybean oil content. The experimental area, consisted of 15 treatments in a completely randomized experimental design. Each plot had size of 20 × 25 m. The treatments consisted of: traditional no-tillage system (control), no-tillage system with application of gypsum, no-tillage with scarification and 12 treatments with cover species called soil structure reclaimers. Soil samples were collected in the layers of 0-0.10; 0.10-0.20 and 0.20-0.30 m, with four replicates. The physical attributes evaluated were bulk density, total porosity, microporosity, macroporosity and saturated hydraulic conductivity in the periods of 2014, 2015 and 2016. In the soybean crop the grain yield, oil content, weight of 100 grains, average height of plants and number of plants/m were evaluated in each treatment with four replications. The oil content was performed by the low-field nuclear magnetic resonance method. The averages of the treatments were compared by the Tukey test at 5% of significance. The results showed that five months after soil scarification did not affect bulk density. Eleven months after gypsum application discrete improvements in density, total porosity, microporosity and soil hydraulic conductivity occurred in the 0-0.10 and 0.10-0.20 m layers. It was also concluded that grain yield, oil content, weight of 100 grains and number of plants per meter were not influenced by the soil cover species and soil management systems.
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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 | 0.000 |
| 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) | 0.001 | 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".