Emissions monitoring at a deep-pit swine finishing facility: Research methods and system performance
Bibliographic record
Abstract
This paper describes part of a comprehensive National Air Emissions Monitoring Study (NAEMS) conducted at a swine finishing farm located in the state of Indiana, in the United States. The NAEMS was a 2-year study of emissions from animal feeding operations that produce pork, chicken meat, eggs, and milk. It provided emission data for the U.S. Environmental Protection Agency (EPA) to develop tools for estimating emissions from livestock farms. The study in Indiana focused on quantifying and characterizing emissions of gases, particulate matter (PM), and volatile organic compounds (VOCs) from a swine finishing quad (four 1000-head rooms under one roof). Long-term continuous and quasi-continuous measurements were conducted with 157 on-line measurement variables using an array of instruments and sensors for gas and PM concentrations, fan operation, room static pressures, indoor temperature and humidity, animal activity and feeding times, and weather conditions. Pig inventory and weight, feed type and quantity, and manure accumulation and composition were also documented. Systematic tests of the measurement system were conducted. Monitoring methodologies, instrumentation applications, equipment maintenance, quality controls, and system performances are presented and can be used as a reference in assessing research quality and improving future environmental studies on livestock facilities. Implications: Aerial pollutant emissions became a major environmental issue for livestock operations after farms increased significantly in size. Gas and particulate matter emissions from livestock production may cause various environmental impacts such as odor and noxious gas exposure in neighboring areas. In this study, comprehensive and long-term emissions of gases and PM at a Midwestern U.S. commercial swine finishing farm were continuously monitored for 2 years. The methodologies, quality assurance procedures, and results of this study can be used to plan future emission monitoring projects, improve baseline emission databases, and validate emission models.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".