Interpretation of soil enzyme activities in a comparison of tillage practices along a topographic and textural gradient
Bibliographic record
Abstract
Assessment of the impacts of soil conservation practices requires measurement of the resulting changes in soil quality at scales inclusive of soil variability comparable to that of typical farms. The objective of this study was to compare changes in six soil enzyme activities (urease, glutaminase, phosphatase, arylsulfatase, β-glucosidase and dehydrogenase) and organic carbon (OC) content resulting from implementation of no-till along a topographic and soil textural gradient. Activities of β-glucosidase, glutaminase, phosphatase and arylsulfatase, and OC content were greater in coarse-textured soils at a lower slope-position than in fine-textured soil at an upper slope-position. Tillage practice influenced activities of urease, glutaminase, β-glucosidase and dehydrogenase but not OC content. The effect of tillage practice on enzyme activities was influenced by sampling depth and slope-position. Phosphatase and arylsulfatase activities of the Ap horizon behaved as indices of soil organic matter (SOM) content along the topographic gradient. Urease and dehydrogenase activities behaved as indices of soil biochemical activity within the Ap horizon. When interpreted in these terms, soil enzyme activities are comparable to other integrative measurements and as such describe system behavior and attributes. Key words: Soil enzyme activity, no-till, topographic position
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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) | 0.000 | 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 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".