Bibliographic record
Abstract
Ammonia (NH3) is emitted in vast quantities from exposed livestock manure. The volatilisation of NH3 from livestock manure is a loss in valuable nitrogen in land-applied manure that could otherwise be used for crop production. Ammonia loss to air is also affiliated with environmental problems when it is deposited to the surrounding landscape. The goal o f this study was to quantify the effect of managing beef cattle manure on NH3 emissions of land-applied manure. Three trials were conducted where beef feedlot manure was applied. The NH3 losses were measured from field plots (90 or 160 m2) using acid traps (passive flux samplers). Immediately after applying manure, irrigating with 6 mm of water reduced NH3 loss by 21–52% while tillage (to 15 cm depth) reduced the loss by 76–85% compared with leaving the manure spread on the soil surface. Piled manure that was applied to the land lost 27% less NH3 than did manure taken directly from the pen. There was little NH3 lost from compost that was applied to land since the applied available-N was very low relative to the pen and piled manure. Our study shows that management of livestock manure has a direct impact on NH3 loss to air. It follows that significant reduction in NH3 volatilisation can benefit agriculture and reduce agriculture’s impact on the environment. Key words: Ammonia, manure, tillage, irrigation, compost, feedlot
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 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".