Ammonia emissions from dairy production in Wisconsin
Bibliographic record
Abstract
Ammonia gas is the only significant basic gas that neutralizes atmospheric acid gases produced from combustion of fossil fuels. This reaction produces an aerosol that is a component of atmospheric haze, is implicated in nitrogen (N) deposition, and may be a potential human health hazard. Because of the potential impact of NH3 emissions, environmentally and economically, the objective of this study was to obtain representative and accurate NH3 emissions data from large dairy farms (>800 cows) in Wisconsin. Ammonia concentrations and climatic measurements were made on 3 dairy farms during winter, summer, and autumn to calculate emissions using an inverse-dispersion analysis technique. These study farms were confinement systems utilizing freestall housing with nearby sand separators and lagoons for waste management. Emissions were calculated from the whole farm including the barns and any waste management components (lagoons and sand separators), and from these components alone when possible. During winter, the lagoons' NH3 emissions were very low and not measurable. During autumn and summer, whole-farm emissions were significantly larger than during winter, with about two-thirds of the total emissions originating from the waste management systems. The mean whole-farm NH3 emissions in winter, autumn, and summer were 1.5, 7.5, and 13.7% of feed N inputs emitted as NH3-N, respectively. Average annual emission comparisons on a unit basis between the 3 farms were similar at 7.0, 7.5, and 8.4% of input feed N emitted as NH3-N, with an annual average for all 3 farms of 7.6 +/- 1.5%. These winter, summer, autumn, and average annual NH3 emissions are considerably smaller than currently used estimates for dairy farms, and smaller than emissions from other types of animal-feeding operations.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".