Estimating enteric methane production for beef cattle using empirical prediction models compared with IPCC Tier 2 methodology
Bibliographic record
Abstract
The Intergovernmental Panel on Climate Change, Tier 2 methodology and 16 empirical models together with dietary information were used to estimate daily methane (CH4) production and Ym (CH4 energy expressed as a percentage of gross energy intake) for mature cows (lactating and dry) and growing steers (backgrounding, grazing, and finishing) in eastern and western Canada. Monthly simulations accounted for changes in body weight, feed intake, and diet composition. Coefficient of variation (CV) and uncertainty (95% confidence interval divided by mean) were used to estimate variability. Estimates of CH4 (g d−1) and Ym from models differed from IPCC estimates. For models, the CV of Ym ranged from 0.8% to 29.7% and uncertainty from 0.9% to 45.2% over the production phases of the animals in contrast to the fixed Ym used by IPCC. When information on diet composition is lacking, a Ym value of 7.0%–7.3% can be used for beef cows depending on stage and location, and 6.4%–6.6% for growing cattle fed high-forage diets, whereas 4.8% is recommended for finishing diets instead of the default values of 6.5% for high-forage diets and 3.0% for finishing diets typically used in the IPCC Tier 2 method.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".