Seasonal Ammonia Emissions from a Free-Stall Dairy in Central Texas
Bibliographic record
Abstract
Studies show that agricultural and animal feeding operations (AFOs) contribute a considerable amount of ammonia (NH 3 ) to the atmosphere.Agricultural NH 3 emissions are recognized as an important air quality issue.Biological decomposition of manure from dairy operations results in emissions of NH 3 and other gases.There is a need to determine NH 3 emission factors (EFs) to compile annual NH 3 inventories.NH 3 emissions should be estimated from different ground-level area sources (GLAS) including open-lots (cows on earthen corrals), free-stalls (cows in barns), manure composting sites, primary and secondary lagoons, separated solids, and milking parlors.A protocol using flux chambers was used to determine NH 3 EFs from different GLAS of a free-stall dairy in central Texas.Data including NH 3 emissions from GLAS were collected during winter and summer seasons.NH 3 concentration measurements were made using chemiluminescence-based analyzers.The EFs for the free-stall dairy were estimated as 11 Ϯ 4.9 (confidence interval [CI]) kg-NH 3 ⅐ yr Ϫ1 ⅐ head Ϫ1 for summer and 4.7 Ϯ 4.9 kg-NH 3 ⅐ yr Ϫ1 ⅐ head Ϫ1 for winter.The estimated annual NH 3 EF was 8.4 Ϯ 4.9 kg-NH 3 ⅐ yr Ϫ1 ⅐ head Ϫ1 for this free-stall dairy.This seasonal difference was attributed to temperature, loading rate of dairy waste, and manure bacterial activity of GLAS.In winter, composted manure and free-stalls contributed nearly 77% of the total NH 3 emissions for the dairy; however, in summer, two lagoons at the dairy contributed 65% of the overall NH 3 emissions.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 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".