Impact of Management on the Physical Attributes of a Dystrophic 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
Soil use and management systems aim to create conditions that are favorable to crop growth. The hypothesis is that areas subject to intensive use of agricultural machinery and animal trampling tend to have a soil structure that is altered by aggregate fragmentation, which causes soil compaction and consequently decreases the soil’s physical and hydraulic properties. The aim of this study was to assess and compare the physical and hydraulic parameters of a dystrophic yellow latosol in an area of Cerrado in the municipality of Chapadinha, Maranhão, Brazil under different use and management systems. The following five use and management systems were studied with five replicates: native forest (control), slash-and-burn agriculture, grassland, no-till crop production and conventional tillage. Data analysis was performed using a completely randomized experimental design. The soil’s density, macroporosity, microporosity, total porosity, hydraulic conductivity, infiltration, water retention curve, penetration resistance and Soil quality assessment index (S index) were assessed for all management systems. The soil use and management systems were found to have a significant effect on the penetration resistance and the water infiltration rate. The native forest and slash-and-burn agriculture areas provided the highest soil water infiltration rates and the lowest soil penetration resistance. A multivariate analysis identified the variables associated with each soil use and management system. The slash-and-burn agriculture area had the highest S index, which means it provided soil of the best physical quality.
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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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