TECHNICAL REPORTS Atmospheric Pollutants and Trace Gases Atmospheric Ammonia, Volatile Fatty Acids, and Other Odorants near Beef Feedlots
Bibliographic record
Abstract
ABSTRACT Emissions from livestock can affect human and ani-mal health. For example, Thu et al. (1997) reportedIntensive livestock operations can release odorous gases from that in poorly ventilated swine (Sus scrofa) barns, highstored or land-applied manure. We measured concentrations of dust and 14 odor-causing gases at increasing distances from four feedlots emissions coincided with symptoms associated with near Lethbridge, southern Alberta, Canada. Concentration was deter- toxic or inflammatory effects on the respiratory tract of mined from the amount of total dust or gas accumulated in the sam- the workers. In addition, residents living downwind may plers, and the volume of air sampled. Adjacent the feedlots, the show increased eye irritation, nausea, weakness, or, in maximum concentration of many volatile fatty acids exceeded re- some instances, psychological responses (Thu et al., ported odor detection thresholds; the maximum ammonia concentra- 1997; Schiffman et al., 1995). High concentrations of tion was close to the threshold. Ammonia and butyric acid approached ammonia inside barns can reduce animal productionor exceeded their individual odor thresholds as far as 200 m downwind (Drummond et al., 1980) and airborne particles in feed-of the feedlots. Highest concentrations were measured adjacent to lots coincide with a higher incidence of pneumonia inland where manure was being applied. None of the odorant concentra-cattle (Bos taurus) (MacVean et al., 1986).tions exceeded their irritation threshold. There was a positive relation-ship between ammonia concentration and odor intensity as well as Livestock operations are prominent sources of atmo-
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.034 | 0.015 |
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".