Visual Evaluation of Soil Structural and Sugarcane Root Under Deep Strip-till and Conventional Tillage
Bibliographic record
Abstract
The Visual Evaluation of Soil Structure (VESS) is a relatively simple methodology used for comparing management systems and for maintaining or recovering the quality of agricultural soils. The objective of this study was to evaluate the structural soil quality in the production of sugarcane using VESS. Three treatments were established: Deep Strip-till (DST), Conventional Tillage (CT) and Uncultivated area (UC). For DST and CT soil samples were taken from two locations: in-row and inter-row. Soil blocks were extracted from mini-trenches and carefully fragmented into aggregates, whose appearance, resistance, and characteristics of the structural units define quality scores. The density of visible roots was quantified by a grid-based counting method. DST at in-row location had improved the structural quality of the soil, providing greater root growth. Scores of visual soil quality in CT showed no difference between in-row and inter-row locations. Preserved from machinery traffic the in-row trail in CT did not result in benefit to soil quality. Variability in the scores among the replicate blocks for DST in-row suggests that the equipment had produced irregular soil tillage. VESS proved to be a good indicator from which it is feasible to evaluate impacts of agricultural machines and tillage implements on soil quality.
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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".