Enzyme activity and C and N pools in soil following application of mulches
Bibliographic record
Abstract
A field experiment was carried out to evaluate the effects of some mulching materials on soil biochemical properties that represent a component of soil quality and functionality. Wheat straw, dairy manure compost, and black polyethylene were used in the mulching experiments of a soil seeded with forage maize. Soil samples were taken from the surface (0–10 cm) and subsurface (10–20 cm) layers 1 and 4 mo after treatments were applied. Soil productivity, biochemical and chemical properties were determined. Compost treatment showed the highest agronomic yield due to the release of nutrients for plant nutrition and soil metabolism. Dehydrogenase activity was used as a measurement of overall microbial activity, whereas β-glucosidase and urease hydrolase activities were determined as an expression of C and N cycles. In addition, a ratio between dehydrogenase activity and water soluble carbon (defined as “metabolic potential index”) was calculated, in order to better represent soil metabolic potential. Compost-mulch treatment showed the lowest ratio values, while straw mulch had the highest value. A similar trend was found for β-glucosidase activity. The data confirmed the validity and sensitivity of the metabolic potential index and β-glucosidase enzyme in monitoring the change of soil biochemical properties and of soil quality during soil management practices. Key words: Soil enzyme activities, mulching practice, 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) | 0.001 | 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".