Abundance, diversity and functional gene expression of denitrifier communities in adjacent riparian and agricultural zones
Bibliographic record
Abstract
Lands under riparian and agricultural management differ in soil properties, water content, plant species and nutrient content and are therefore expected to influence denitrifier communities, denitrification and nitrous oxide (N(2) O) emissions. Denitrifier community abundance, denitrifier community structure, denitrification gene expression and activity were quantified on three dates in a maize field and adjacent riparian zone. N(2) O emissions were greater in the agricultural zone, whereas complete denitrification to N(2) was greater in the riparian zone. In general, the targeted denitrifier community abundance did not change between agricultural and riparian zones. However, nosZ gene expression was greater in the riparian zone than the agricultural zone. The community structure of nirS-gene-bearing denitrifiers differed in June only, whereas the nirK-gene-bearing community structure differed significantly between the riparian and the agricultural zones at all dates. The nirK-gene-bearing community structure was correlated with soil pH, while no significant correlations were found between nirS-gene-bearing community structure and soil environmental variables or N(2) O emissions, denitrification or denitrifier enzyme activity. The results suggested for the nirK and nirS-gene-bearing communities different factors control abundance vs. community structure. The nirK-gene-bearing community structure was also more responsive than the nirS-gene-bearing community structure to change between the two ecosystems.
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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 | 0.001 |
| 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".