Seasonal changes in the abundance and activity of bacterial and fungal denitrifying communities associated with different compost amendments
Bibliographic record
Abstract
Composts can be efficient organic amendments in potato culture as they can supply carbon and nutrients to the soil. However, more information is required on the effects of composts on denitrification and nitrous oxide emissions (N2O) and emission-producing denitrifying communities. The effects of three compost amendments (municipal source separated organic waste compost (SSOC), forestry waste mixed with poultry manure compost (FPMC), and forestry residues compost (FRC)) on fungal and bacterial denitrifying communities and activity was examined in an agricultural field cropped to potatoes during the fall, spring, and summer seasons. The denitrification enzyme activity (DEA), N2O emissions, and respiration were measured in parallel. N2O emission rates were greater in FRC-amended soils in the fall and summer, whereas soil respiration was highest in the SSOC-amended soil in the fall. A large number of nirK denitrifying fungal transcripts were detected in the fall, coinciding with compost application, while the greatest nirK bacterial transcripts were measured in the summer when plants were actively growing. Denitrifying community and transcript levels were poor predictors of DEA, N2O emissions, or respiration rates in compost-amended soil. Overall, the sampling date was driving the population and activity levels of the three denitrifying communities under study.
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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 | 0.000 | 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".