Universally applicable methane prediction equations for beef cattle fed high- or low-forage diets
Bibliographic record
Abstract
Universally applicable empirical equations specific for high- and low-forage diets were developed to improve the prediction of enteric methane production (eCH4) from beef cattle. A database built using treatment means from published beef studies conducted in numerous countries was divided into two datasets: high-forage diet [≥40% forage dry matter (DM), n = 123] and low-forage diet (≤20% forage DM, n = 34). Monte-Carlo techniques were used to overcome the limited numbers of observations in each dataset, and multiple regression analysis and cross validation were used to develop new eCH4 prediction equations. Precision, accuracy, and analysis of errors were evaluated using concordance correlation (rc) and root mean square prediction error (RMSPE). The best-fit equations for high and low forage content included the following variables: body weight (kg) and intakes (kg d−1) of DM, fat, neutral detergent fiber (NDF), acid detergent fiber, crude protein to NDF ratio, and starch to NDF ratio. For high and low forages, best-fit equations had rc ≥ 0.70 and RMSPE ≤ 40 g eCH4 d−1 and rc ≥ 0.90 and RMSPE ≤ 15 g eCH4 d−1, respectively. Use of equations specific to dietary forage proportion reduced the uncertainty of estimating beef cattle eCH4 emission compared with the Intergovernmental Panel on Climate Change Tier 2 methodology.
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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".