Gypsum, Soil Scarification and Succession Planting as Alternatives to Mitigate Compaction of Dystrophic Red-Yellow Latosol
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 it