Greenhouse gas emissions from stored liquid swine manure
Bibliographic record
Abstract
Current global warming has been linked to anthropogenic greenhouse gas (GHG) concentration increases. Environmental and animal factors affect and lend uncertainties to GHG emissions from swine manure. A micrometeorological four-tower mass balance method was used to quantify CH4 and N 2O emissions from stored liquid swine manure in a quasi-continuous year-round study at two commercial swine farms (Jarvis and Guelph) in a cold climate region. Data filtering criteria were developed to minimize errors in flux calculation, and were efficient to improve accuracy of GHG flux estimates. In the Jarvis experiment, CH4 and N2O emissions were significantly higher than zero, with emissions during summer higher than during fall (CH4: 583.8 vs. 174.1 [mu]g/m2/s; N 2O: 337.6 vs. 101.8 ng/m2/s). In the Guelph experiment, only CH4 emissions were significantly larger than zero (1054.8 in fall vs. 22.7 [mu]g/m2/s in winter). Significant differences in daytime and nighttime CH4 fluxes were observed during summer and winter. Each manure storage tank was very heterogeneous, showing 'hot spots' emitting higher CH4. Methane fluxes calculated through IPCC default methods were 1.6 to 6.7 times higher than measured via four-tower mass balance method.
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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 | 0.000 |
| 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) | 0.000 | 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".