Denitrifier Community Dynamics in Soil Aggregates under Permanent Grassland and Arable Cropping Systems
Bibliographic record
Abstract
A better understanding of the spatial distribution of denitrifiers and their activity may lead to an improved understanding of the denitrification process in soil. This study determined the spatial distribution of the total bacterial community (16S rRNA), components of the denitrifier community ( cnorB P ; Pseudomonas mandelii and related species; nosZ ), and denitrification activity across a range of soil aggregate size fractions (4–8, 1–2, and 0.25–0.5 mm) under permanent grassland (PG) and arable cropping (AC) systems. Aggregate size fraction had no significant effect on the abundance of the nosZ or cnorB P gene‐bearing bacteria in soil from the AC system. The highest abundance of denitrifier bacteria was measured in the smallest size fraction in the PG system. Respiration did not differ among aggregate size fractions within the PG system; however, respiration was higher for the PG system than the AC system for all aggregate size fractions. For the AC system, higher respiration was measured in the 0.25‐ to 0.5‐mm aggregate fraction than the 4‐ to 8‐mm aggregate fraction. Denitrifying enzyme activity (DEA) was higher in the largest size fraction of the PG system than the AC system; however, DEA did not differ among aggregate size fractions within each management system. Cumulative denitrification during a 72‐h incubation was significantly higher in the largest aggregate size fractions under both management systems. The results indicate that the differences among the aggregate size fractions were small in magnitude and that the spatial location of the denitrification activity and the abundance of the denitrifier bacteria were uncoupled across aggregate size fractions in the contrasting management systems.
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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) | 0.000 | 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".