The Effect of Phosphogypsum on Greenhouse Gas Emissions during Cattle Manure Composting
Bibliographic record
Abstract
Phosphogypsum (PG), a by-product of the phosphate fertilizer industry, reduces N losses when added to composting livestock manure, but its impact on greenhouse gas emissions is unclear. The objective of this research was to assess the effects of PG addition on greenhouse gas emissions during cattle feedlot manure composting. Sand was used as a filler material for comparison. The seven treatments were PG10, PG20, PG30, S10, S20, and S30, representing the rate of PG or sand addition at 10, 20, or 30% of manure dry weight and a check treatment (no PG or sand) with three replications. The manure treatments were composted in open windrows and turned five times during a 134-d period. Addition of PG significantly increased electrical conductivity (EC) and decreased pH in the final compost. Total carbon (TC), total nitrogen (TN), and mineral nitrogen contents in the final composted product were not affected by the addition of PG or sand. From 40 to 54% of initial TC was lost during composting, mostly as CO(2), with CH(4) accounting for <14%. The addition of PG significantly reduced CH(4) emissions, which decreased exponentially with the compost total sulfur (TS) content. The emission of N(2)O accounted for <0.2% of initial TN in the manure, increasing as compost pH decreased from alkaline to near neutral. Based on the total greenhouse gas budget, PG addition reduced greenhouse gas emissions (CO(2)-C equivalent) during composting of livestock manure by at least 58%, primarily due to reduced CH(4) emission.
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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 | 0.000 | 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".