Assessment of Sprinklers on the Removal Efficiency of Ammonia and Particulate Matter in a Commercial Broiler Facility
Bibliographic record
Abstract
Abstract. Intensive poultry production can be a significant source of airborne pollutants. There are potential health risks posed to poultry and management staff that is frequently exposed to high concentrations of pollutants inside the production facility. Ammonia and particulate matter are of particular interest primarily due to their negative environmental and health effects. A two story commercial broiler facility in Perth County, Ontario, Canada was used for this study. The effects of a sprinkler system on the emissions of ammonia (NH3) and particulate matter (PM10 and PM2.5) were investigated. The duration of the study spanned three seasons: winter, spring, and summer. During the winter, NH3, PM10, and PM2.5 removal efficiencies were calculated to be 68%, 84%, and 60%, respectively. During the spring sampling campaign the removal efficiency of NH3 was calculated to be 7%. Due to an unusually high concentration of PM on the treatment floor during the spring, removal efficiencies for PM could not be calculated. Removal efficiencies for the summer season for NH3, PM10, and PM2.5 were calculated to be 22%, 89%, and 86%, respectively. In the current study water sprinkling systems have been demonstrated to be an effective control technology for reducing emissions of PM and NH3, but are influenced by management practices and the time of year.
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".