Emissions of atmospheric pollutants from a combined commercial dairy barn
Bibliographic record
Abstract
Abstract. Animal agriculture has been trending toward larger scale farms to accommodate the need for animal products. The increase in farm size has created an increased waste stream of solids and gases. The goal of this study was to develop emission factors for methane (CH4), ammonia (NH3), and size fractionated particulate matter (PM10 and PM2.5) from a dairy barn to address knowledge gaps in the Canadian agricultural emissions inventory. A heated sample line conveyed barn air into a trailer that housed a methane/non-methane hydrocarbon flame ionization detector (FID) to sample for CH4 and an NH3 chemiluminescence analyzer. The PM was sampled using an optical particle counter that was placed in the barn. Two sampling runs were conducted during the winter and the spring to compare the effect of seasonal changes. The winter results for NH3, CH4, PM2.5 and PM10 were 2.12, 29.0, 0.0011, 0.00037 g hr-1 AU-1 (AU – animal unit equivalent to 500 kg live mass), respectively. The spring results for NH3, CH4 were 1.82 & 11.6 g hr-1 AU-1 respectively, and for PM2.5 and PM10, 2.0 & 0.91 mg hr-1 AU-1, respectively. In the winter and spring, there are two significant spikes in emissions for CH4 and NH3 occurring between 06:00 and 09:00 and 16:00 and 19:00. In the spring, the spikes are almost equal while, in the winter, the morning spike is much smaller than the afternoon. PM displayed similar seasonal patterns with one significant event at 08:00. There are many activities in the morning that could contribute to the first spike in emissions of all contaminants (e.g. lights turning on, feeding and manure belt activation), but the only major activity in the afternoon is feeding.
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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 | 0.001 | 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) | 0.002 | 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".