Innovative Air Treatment Unit for Swine Exhaust Air - Commercial-Scale Tests
Bibliographic record
Abstract
Swine housing facilities can emit substantial amounts of aerial contaminants, such as ammonia, dust and odour. These emissions can have a significant impact on both the environment and human health. It is also known that by reducing odour emissions, producers can improve their relationship with their neighbors. In this study, a commercial-scale swine exhaust air treatment unit (ATU) was developed and tested under real barn conditions for its effectiveness to reduce the emissions of ammonia, dust and odour. Results from laboratory-scale tests carried out at the IRDA facilities in Quebec were used to design the commercial-scale ATUs. Three of these units were then built using recycled shipping containers and retrofitted to three grower-finisher rooms at the barn facility of the Prairie Swine Centre Inc. in Saskatoon, SK. Each room was filled with 60 pigs and the exhaust air was ducted to an ATU. During the 12-week trial, samples were taken on a regular basis to monitor concentrations of ammonia, dust and odour before and after each ATU. After a short start-up period, the commercial-scale ATUs provided robust and consistent performance under real barn conditions. The maximum removal efficiencies obtained during these tests were 77%, 92% and 75% for ammonia, dust and odour, respectively. However, the results for odour removal were variable with the replicates and with time. The water consumption, used to maintain performance over the duration of the trials, tended to increase as the ATU removed more contaminants from the air.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".