Beef cattle methane emissions measured with tracer-ratio and inverse dispersion modelling techniques
Bibliographic record
Abstract
The development and validation of management practices to mitigate greenhouse gas (GHG) emissions from livestock require accurate emission measurements. This study assessed the accuracy of a practical inverse dispersion modelling (IDM) technique to quantify methane (CH 4 ) emitted from a small cattle herd (16 animals) confined to a 63 m × 60 m experimental pen. The IDM technique calculates emissions from the increase in the CH 4 concentration measured downwind of the animals. The measurements were conducted for 7 d. Two types of open-path (OP) gas sensors were used to measure concentration in the IDM calculation: a Fourier transform infrared spectrometer (IDM-FTIR) or a CH 4 laser (IDM-Laser). The actual cattle emission rate was measured with a tracer-ratio technique using nitrous oxide (N 2 O) as the tracer gas. We found very good agreement between the two IDM emission estimates (308.1 ± 2.1 – mean ± SE – and 304.4 ± 8.0 g CH 4 head −1 d −1 for the IDM-FTIR and IDM-Laser respectively) and the tracer-ratio measurements (301.9 ± 1.5 g CH 4 head −1 d −1 ). This study suggests that a practical IDM measurement approach can provide an accurate method of estimating cattle 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 | 0.001 | 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) | 0.000 | 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".