Methane Emissions from Grazing Cattle Using Point‐Source Dispersion
Bibliographic record
Abstract
The ability to accurately measure greenhouse gas (GHG) emissions is essential to gauge our ability to reduce these emissions. Enteric methane from ruminants is an important but often difficult source to quantify since it depends on the amount and type of feed intake. Unfortunately, many of the available measurement techniques for estimating enteric methane emissions can impose a change in feed intake. Our study evaluates a nonintrusive technique that uses a novel approach (point-source dispersion with multiple open-path concentrations) to calculate enteric methane emissions from grazing cattle, reported as the major source of GHG in many countries, particularly Australia. A scanner with a mounted open-path laser was used to measure methane concentration across five paths above a paddock containing 18 grazing cattle over 16 d. These data were used along with wind statistics in a dispersion model (WindTrax) to estimate an average herd methane emission rate over 10-mm intervals. Enteric methane emissions from the herd grazing a combination of Rhodes grass (Chlotis gayana Kunth) and Leucaena [Leucaena leucocephala (Lam.)] averaged (+/- SD) 141 (+/- 147) g animal(-1) d(-1). In a release-recovery experiment, the technique accounted for 77% of the released methane at a single point. Our study shows the technique generates more reliable methane emissions during daytime (unstable stratification).
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.001 | 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 teacher head, 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".