Measurement of Long-Term CH4 Emissions and Emission Factors from Beef Feedlots in Australia
Bibliographic record
Abstract
One of the major challenges for the Australian feedlot and meat sectors is to have accurate and robust long-term greenhouse gas (GHG) emissions data. Long-term measurements of methane (CH4) emissions (2015–2017) were made at two Australian beef feedlots having different climates, cattle types, and management practices. Emissions were measured using the inverse-dispersion model (IDM) micrometeorological technique, using CH4 concentrations measured at the feedlots with a closed-path Fourier transform infrared spectroscopic technique (CP-FTIR). The emissions data were used to evaluate methods used by the Department of Climate Change, Energy, the Environment and Water to estimate CH4 emissions from feedlots in Australian national inventory calculations. Expressed as a CH4 yield (emissions per unit dry matter intake, DMI), the two feedlots had emissions of 13.1 and 18.9 g CH4 kg−1 DMI. The lower-emitting feedlot had emissions that were 30% lower than the national inventory calculations based on feed intakes, while the second feedlot had emissions that were similar to the inventory calculations. The accurate quantification of emissions from feedlots, as demonstrated as part of this study, is important for validating the national accounting methods and therefore the sector’s GHG emissions profile.
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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) | 0.002 | 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".