Ventilation rates in large commercial layer hen houses with two-year continuous monitoring
Bibliographic record
Abstract
1. Ventilation controls the indoor environment and is critical for poultry production and welfare. Ventilation is also crucial for assessing aerial pollutant emissions from the poultry industry. Published ventilation data for commercial layer houses have been limited, and are mostly based on short-term studies, mainly because monitoring airflow from large numbers of fans is technically challenging. 2. A two-year continuous ventilation monitoring trial was conducted at two commercial manure belt houses (A and B), each with 250 000 layers and 88 130-cm exhaust fans. All the fans were individually monitored with fan rotational speed sensors or vibration sensors. Differential static pressures across the house walls were also measured. Three fan performance assessment methods were applied periodically to determine fan degradations. Fan models were developed to calculate house ventilations. 3. A total of 693 and 678 complete data days, each containing >16 h of valid ventilation data, were obtained in houses A and B, respectively. The two-year mean ventilation rates of houses A and B were 2·08 and 2·10 m(3) h(-1) hen(-1), corresponding to static pressures of -36·5 and -48·9 Pa, respectively. For monthly mean ventilation, the maximum rates were 4·87 and 5·01 m(3) h(-1) hen(-1) in July 2008, and the minimum were 0·59 and 0·81 m(3) h(-1) hen(-1) in February 2008, for houses A and B, respectively. 4. The two-year mean ventilation rates were similar to those from a survey in Germany and a 6-month study in Indiana, USA, but were much lower than the 8·4 and 6·2 m(3) h(-1) hen(-1) from a study in Italy. The minimum monthly mean ventilation rates were similar to the data obtained in winter in Canada, but were lower than the minimum ventilation suggested in the literature. The lower static pressure in house B required more ventilation energy input. The two houses, although identical, demonstrated differences in indoor environment controls that represented potential to increase ventilation energy efficiency, and reduce carbon footprints and operational costs.
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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 | 0.000 |
| 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) | 0.000 | 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".