Estimation of ammonia emission episodes for a national inventory using a farmer survey and probable number of field working days
Bibliographic record
Abstract
Emissions of ammonia (NH3) and odor from livestock operations, and particularly from the landspreading of manure, are an issue from many aspects. Regional and national issues include production of inhalable aerosols (PM2.5), where NH3 from agriculture is a critical precursor. This process is episodic. As a result, it is important to characterize the episodic nature of NH3 emissions. This paper addresses the fact that weather and soil conditions can focus manure landspreading into a relatively few days each year. The Versatile Soil Moisture Budget (VSMB) was used with 30 yr of weather data on 2576 soil landscape units in Canada to estimate the number of days in each month when manure spreading would be possible. In addition, a national survey of about 3100 livestock farmers was used to quantify common practice. The stochastic information from the VSMB and the survey were applied to an NH3 emissions model. The base case, invoked by most national NH3 emission inventory calculations, is that emissions are uniformly distributed throughout each month. The result of our study was that daily NH3 emission fluxes could be up to 20-fold higher in the spring and fall months when manure landspreading is common but when weather and soil conditions limit the number of days available for field work. This has direct implications for estimating the role of agricultural NH3 on the episodic production of PM2.5, and this approach also has application to odor management. Key words: Modeling, livestock, emission factor, odor, NH3, field working day
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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 | 0.001 |
| 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 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".