Influence of Building Maintenance, Environmental Factors, and Seasons on Airborne Contaminants of Swine Confinement Buildings
Bibliographic record
Abstract
Eight swine confinement buildings, selected to cover the widest possible range of cleanliness, were visited twice during winter and once during summer to verify the range, seasonal variations, and correlations between biological and chemical contaminants. Physical aspects were graded for dirtiness (1=clean, 10=dirty), ventilation, air temperature, number of animals, building, and room size. Air samples were taken to measure relative humidity, CO2, ammonia, total dust, and microbiological counts and/or identification (bacteria and molds); endotoxin levels also were measured. During winter, average measurements and ranges were: CO2=0.304% (0.254 to 0.349%); ammonia=19.6 ppm (1.9 to 25.9 ppm); dust=3.54 mg/m3 (2.15 to 5.60 mg/m3). There were 883 cfu/m3 (547 to 2862 cfu/m3) of molds, 4.25×105 cfu/m3 (1.67×105 to 9.30×105 cfu/m3) of total bacteria, 29 cfu/m3 (3 to 94 cfu/m3) of thermophilic actinomycetes). A significant decrease in bacterial levels (p=0.04), dust (p=0.0008), ammonia (p=0.005), and CO2 (p<0.0001) was observed during summer sampling when compared with winter levels. Mold counts were positively correlated (p=0.03) with dirtiness scores, while bacterial counts were negatively correlated with this parameter (p<0.002), whereas bacteria and endotoxins were correlated with the number of animals (p<0.05). Ambient gases (CO2 and ammonia) correlated with each other (p=0.006). Bacteria were the most important contaminant in swine confinement buildings, and endotoxin levels found were also very high (mean=4.9×103 EU/m3). We conclude that a wide range of air contamination exists in swine confinement buildings of different maintenance. There is a decrease in some of these contaminants during summer. Observed dirtiness of the swine confinement buildings has a poor predictive value concerning air quality.
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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) | 0.000 | 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 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".