Development of ammonia emission factors for the land application of poultry manure in the lower fraser valley of British Columbia
Bibliographic record
Abstract
Lau, A. K., Bittman, S. and Hunt, D. E. 2008. Development of ammonia emission factors for the land application of poultry manure in the Lower Fraser Valley of British Columbia. Canadian Biosystems Engineering/Le genie des biosystemes au Canada 50: 6.47 6.55. The objective of this study was to monitor ammonia emissions and develop updated ammonia emission factors for land application of poultry manure under British Columbia conditions. Field trials, which involved four types of poultry manure as the fertilizer materials, were performed at Agassiz Research Station in 2005 and 2006. After manure application, ammonia emission rates were determined using wind tunnels, capturing the emitted ammonia with acid traps and analyzing with a flow injection analyzer. For all trials, the highest emissions occurred within the first day, and gradually declined over the next 2-3 weeks. Cumulative ammonia emission in all treatments did not exceed the initial amount of ammonianitrogen present in manure. Ammonia emission rates were significantly different among the manure types (pB0.005). The percent total loss of ammonia with time was positively correlated with manure pH. Ammonia emission rates were generally higher in both of the spring trials than the fall trial. The proposed revised ammonia emission factors of 0.12 and 0.16 for the two major types of poultry broiler and layer are in line with current emission factors adopted by Environment Canada. However, current and revised emission factors (0.38 vs. 0.13) were substantially different for turkey manure.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".