Gypsum, Soil Scarification and Succession Planting as Alternatives to Mitigate Compaction of Dystrophic Red-Yellow Latosol
Bibliographic record
Abstract
Understanding and quantifying the impact of soil management and use on its physical properties are essential to the development of sustainable agricultural systems. Thus, the aim of this study was to assess the effect of agricultural gypsum, soil scarification and succession planting on the physical attributes of dystrophic red-yellow latosol in Porto Velho, Rondônia state (RO), Brazil. The treatments used were absence and application of 2000 kg ha-1 of gypsum, absence and use of soil scarification, and three types of crop succession: SF (soybean/fallow), SMF (soybean/maize/fallow) and SMBF (soybean/maize/brachiaria/fallow). A randomized block design was used on eight blocks, for a 2 × 2 × 3 factorial arrangement. Soil parameters assessed were macroporosity, microporosity, total porosity, soil density, moisture content and penetration resistance. Data normality was assessed using the Shapiro-Wilk test. The data were submitted to analysis of variance and means were compared by the Scott-Knott test at 5% probability. The highest macroporosity and total porosity values were recorded in treatments with gypsum application and soil scarification. Penetration resistance was lower in the SMBF and SMF crop successions. There was no treatment effect on the soil density.
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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 | 0.000 | 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".