Enhanced Dispersion and Removal of Ammonia Emitted from a Poultry House with a Vegetative Environmental Buffer
Bibliographic record
Abstract
Vegetative environmental buffers (VEBs), which are composed of tolerant trees, shrubs, and tall grasses, can be used to control and reduce the transport of ammonia (NH3) emissions from animal feeding operations (AFOs). However, the effectiveness of VEBs has not been quantitated. In this study, we measure the dispersion and removal of NH3 in simulated emissions from a small broiler house that was equipped with a VEB. The dispersion enhancement due to the VEB was estimated by comparing the measured downwind concentration of the co-released tracer gas, methane (CH4), to the theoretical CH4 concentrations at the same distance downwind without the VEB. The accuracy of the theoretical downwind concentrations calculated using the forward Lagrangian stochastic (fLS) technique was 95%, which was validated by comparing the measured and calculated CH4 concentrations in a separate experiment without the VEB. The VEB enhanced the dispersion of CH4 and reduced the downwind concentration to 63% of the theoretical concentration. In addition to dispersion, the VEB removed another 22% of the NH3, resulting in a net 51% decrease of the theoretical downwind concentration. These results clearly demonstrated that the VEB was effective both in dispersing and removing NH3 emitted from the broiler house.
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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) | 0.000 | 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".