Nitrous oxide production and nitrogen transformations in a soil amended with biosolids
Bibliographic record
Abstract
The application of organic amendments to agricultural soils enables the recycling of nutrients, further reducing the inputs of synthetic fertilizers for crop production. However, the production of N2O emissions is a concern that arises from such a practice. A 35 d incubation experiment was conducted with soils receiving three contrasting types of biosolids — mesophilic anaerobic digested (BM), composted (BC), and alkaline-stabilized (BA) — at four water-filled pore spaces (WFPS): 28%, 40%, 52%, and 64%. A zero-N-addition control was also evaluated. Across all the three types of biosolids, N2O production increased with soil moisture content, with BM and BC producing the overall highest N2O fluxes. The most intense pulses of N2O production were exhibited by BC at the beginning of the incubation. The highest cumulative N2O production was found with 64% WFPS and from BC- (409 μg N2O–N·kg−1 soil) or BM-amended soils (390 μgN2O–N·kg−1 soil), which produced more than four and two times the emissions from the control and BA-amended soils at 64% WFPS, respectively. We also found the highest nitrification rates in the BM- and BC-amended soils. The total N2O production was exponentially associated with the NO3−–N concentration present at the end of the experiment (R2 = 0.83). Changes in the concentration of the soil available N indicated the occurrence of mineralization, nitrification, and denitrification over the incubation. These results provided insight into the interacting responses of N2O production to soil moisture contents, biosolids treatment stabilization and properties, and soil N availability.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".