Pilot-scale electro-nanospray system for decontamination of pig barns
Why this work is in the frame
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Bibliographic record
Abstract
Exposure to high concentrations of bioaerosols, noxious gases, and airborne dust in confined livestock facilities has been linked to human and animal health hazards. Thus, air treatment in animal production facilities is crucial. However, most technologies are either costly or toxic to workers, animals, and the environment. This pilot-scale study assessed the performance of an electro-nanospray system in decontaminating pig barns. Two identical mechanically ventilated pig rooms (one control room and one treatment room) were used, each with four grower/finisher pigs under conditions similar to those in commercial barns. Four electro-nanospray units were installed in the treatment room, and air samples were collected from both rooms over a period of eight weeks to evaluate the performance of the electro-nanospray system in reducing airborne and surface contaminant levels in pig barns. No significant differences in the concentrations of airborne microorganisms ( P = 0.739), dust ( P = 0.537), and ammonia ( P = 0.103) were observed between the two rooms, despite reductions of 18 %, 16 %, and 18 %, respectively. However, significant differences in microbial concentrations on concrete ( P = 0.004), metal ( P = 0.009), wood ( P = 0.022), and plastic ( P = 0.002) surfaces were observed between the two rooms, with reductions of 55 %, 50 %, 52 %, and 59 %, respectively. The treatment had no significant impacts ( P > 0.05) on pigs’ daily water consumption, feed intake, and weight gain. The electro-nanospray system showed potential in decontaminating pig barn air and surfaces. • An electro-nanospray system reduced microorganisms, dust, and ammonia in a pig room. • The nanospray system decontaminated barn surfaces. • Airborne bacteria and dust concentrations were positively correlated. • The nanospray treatment had no significant impact on animal performance.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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 it