Short‐Term Effects of Mineral and Organic Fertilizer on Denitrifiers, Nitrous Oxide Emissions and Denitrification in Long‐Term Amended Vineyard Soils
Bibliographic record
Abstract
Short‐term effects (i.e., 21 d) of mineral or organic fertilizer application on long‐term (i.e., 8 yr of applications) amended soil on denitrifier community abundance, denitrification gene mRNA transcript numbers, denitrification rate, and emissions of N 2 O were explored. Soil was collected from a vineyard in Italy receiving annual applications of either mineral fertilizer (conventional management system, CS) or municipal compost (organic management system, OS). Each soil was incubated using three treatments: no amendment, NH 4 NO 3 , or municipal compost. Microcosms set up with soil treated with compost showed higher nirS , nirK , and nosZ abundance in comparison to conventional fertilization. Short‐term compost addition increased nirK gene abundance over time in OS and CS soils, whereas nirS and nosZ gene abundance increased after compost addition only in OS soil. In OS soil, nosZ gene mRNA transcript numbers were higher at all time‐points for all treatments compared with CS soil. Furthermore, nosZ gene mRNA transcript number increased over time after compost addition for both soils, N 2 O emissions were higher in both soils after NH 4 NO 3 addition compared with no amendment and compost addition. Denitrification was higher in OS than CS soil following NH 4 NO 3 treatment. Denitrification rates were much higher than N 2 O rates in all cases suggesting most emissions occurred as N 2 . Our study demonstrated that long‐term urban‐waste compost application clearly changed soil denitrifier communities and the response of denitrification and N 2 O emissions to different short‐term soil amendments.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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 teacher head, 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".