REDUCTION OF ODOUR EMISSIONS FROM SWINE BUILDINGS: COMPARISON OF THREE REDUCTION TECHNIQUES
Bibliographic record
Abstract
Although several technologies have been developed to reduce odour emissions from swine housing, there is actually no official inventory that can be used to compare the reductions obtained with these technologies. The objectives of the present study were 1) to select from the literature the most promising technologies allowing odour emissions reduction; 2) to evaluate the selected technologies in an experimental barn and 3) to compare the emission reductions. A literature review listing all developed technologies was carried out. A list of criteria classified within six different categories (agronomic, economic, technical, environmental, use of resources and social & health) was used to assess the global potential performance of all those technologies. An experimental laboratory farm was then use to compare the selected technologies. The experiment involved five treatments: diet alterations, under slats separation system, a combination of diet alterations and under slats separation, biological air treatment and a control. Identical and independent chambers housing four growing-finishing pigs were monitored during seven weeks (3 weeks of accommodation and 4 weeks of testing). Odour concentration was evaluated weekly by olfactometry. Preliminary results showed that an appropriate diet can reduce odour emissions by 35% while in-barn separation system allowed a 33% reduction of the odour emissions. The combination of these two treatments allows a total reduction of 59% of the odour emissions. The biological air treatment can reduce the odour emissions by 81%. Results indicate that a biological air treatment is the most promising technology for odour reduction from swine buildings.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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 teacher head, 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".