Greenhouse gas emissions from decomposition of biosolids as affected by stabilization method and soil moisture
Bibliographic record
Abstract
Abstract Computational models are useful to estimate agricultural greenhouse gas emissions at regional scales. However, empirically based parameter values are required for the models to accurately represent carbon (C) and nitrogen (N) mineralization rates of different organic amendments in more and less humid regions or during wet and dry periods of the growing season. A controlled environment study was conducted to assess the rates of C and N mineralization in differently processed sewage sludge (biosolids) in wet and dry soil. Parameter values were estimated for use in modelling the degradation of three types of biosolids. A loam soil with either 49% water‐filled pore space (WFPS) or 29% WFPS was amended with mesophilic anaerobically digested (digested), alkaline‐stabilized, or composted biosolids. Headspace samples were collected and analysed for carbon dioxide (CO 2 ) and nitrous oxide (N 2 O), and soil samples for nitrate () and ammonium (). Four different first‐order models were fitted to the cumulative CO 2 –C and N 2 O–N data (R 2 > 0.98), and soil (R 2 > 0.65) and (R 2 > 0.93) concentrations. CO 2 –C data indicated that C mineralization was higher in soil with 49% WFPS than in soils with 29% WFPS. Seventy‐nine percent of the C compounds in digested biosolids degraded in soil with 49% WFPS, compared with 52% for alkaline‐stabilized biosolids and 8% for composted biosolids. The fitted coefficient values were similar for all of the four first‐order models used in this study and provide useful information for parameterizing more sophisticated mechanistic models of the degradation of biosolids in soil.
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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 | 0.001 |
| 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) | 0.000 | 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".