Soil Physical Quality of Brazilian Crop Management Systems Evaluated with Aid of Penetrometer
Bibliographic record
Abstract
Crop management affects soil attributes as well as its quality. We evaluated the following soil physical attributes: saturated hydraulic conductivity (K0), soil resistance (RP) and soil bulk density (BD), in Araras-SP, Brazil. Areas with sugarcane (Saccharum officinarum), soybean (Glycine max), physic nut (Jatropha curcas L.) and native forest presented an increase of soil compaction in the 0.10 m surface layer for the three attributes in a following order: native forest < soybean < sugarcane. Significant regressions were obtained for RP × K0; BD × K0 and BD × RP. Penetrometer measurements were essential to indicate differences among areassugarcane, native forest, physic nut and soybean; but for the measurements of K0, only between sugarcane and native forest. RP measurements confirm anthropogenic changes in the soil profile up to the 0.3 m depth. In the “Canarache soil resistance classification” soils showed “low resistance” “without limitations to root development” for native forest and physic nut; “medium resistance” for soybean area with “some limitations to root development” and “high resistance” for sugarcane with “limitations to root development”. The use of penetrometers is discussed in relation to the readiness of field measurements.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".