Characterization of Indoor AtmosphericNitrogenous Chemicals in Poultry Farms
Bibliographic record
Abstract
Indoor air pollution is seen in poultry and many other animal husbandry industries. Small airborne nitrogenous chemicals (ANCs), such as ammonia and small amines, are common air pollutants in poultry farms. Elevated ANC concentration in poultry farms can significantly worsen the indoor air quality (IAQ) of the farm, which will affect animal productivity, animal welfare, and occupational health of producers. Re- cent studies have identified ammonia and small volatile organic pollutants in the farm. On the other hand, characterization of large ANCs, such as uric acid (UA) and large amines have rarely been reported, despite they are proposed as the major source of biological nitrogen waste. Our goal is to project a novel insight into nitrogen cycles in poultry farms. This project includes on-site time-resolved collections of ANCs using a particle-into-liquid-sampler (PILS), followed by chemical characterization by liquid chromatography-mass spectrometry (LC-MS) with a novel derivatization method. Over quantitative assessment of ANCs in the poultry farm, we discovered UA and suspended particles are correlated with changing animal behaviors. Phase partition- ing of UA, ammoniacal species, and large amines were discovered among air, particle, and litter materials. The discovery of these indoor pollutants can be associated with the formation of dust particles and ammonia, and the results can benefit the poultry industry in solving persisting IAQ problems.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 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".