Across study evaluation of enteric methane emissions from dairy cattle for spot sampling schemes using simulation approaches
Bibliographic record
Abstract
Measuring daily enteric methane emissions from dairy cattle using spot sampling systems such as GreenFeed requires careful consideration of sampling frequency and timing. Previous research shows that measurement accuracy increases with sampling frequency, and a minimum number of observations is essential, particularly for restricted-fed dairy cattle. The present study evaluated the accuracy of various sampling schemes using simulation approaches across multiple experiments. Diurnal methane emission patterns from 6 in vivo respiration-chamber experiments were compiled. Diets included grass herbages, corn and grass silages and linseed oil and 3-NOP supplements. Cattle were fed either restrictively at 80–95% of ad libitum intake or fully ad libitum. Methane emissions were recorded over two or three 24-hour periods at ≤ 20-minute intervals. For each animal, the mean of all observed diurnal emission rates was converted to daily methane production (g/d) and treated as the reference. Fourteen preset sampling schemes were evaluated, including 10 evenly spaced intervals (e.g., every 0.5 to every 8 hours) and 4 uneven intervals based on prior literature. Daily methane production calculated for each sampling scheme was statistically compared with the reference using mixed models, with sampling and dietary treatment included as fixed effects and cow as a random effect. To further assess sampling precision, generalized additive models were fitted to diurnal methane profiles, and areas under the curve were compared with reference means. Using the best-fitting spline for each profile, resampling under three schemes was performed 1,000 times to estimate means and standard deviations of methane production, and next construct 95% confidence intervals relative to sample size. Results show that hourly or specific 2- or 3-hour sampling schedules provide accurate estimates of daily methane production, especially in restricted-feeding systems. Although increasing sample size narrows confidence intervals, the choice of sampling scheme consistently influences precision across experiments.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".