Performance of a Dispersion Model to Estimate Methane Loss from Cattle in Pens
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Bibliographic record
Abstract
Accurate measurements of enteric methane (CH(4)) emissions from cattle (Bos taurus) are necessary to improve emission coefficients used in national emissions inventories, and to evaluate mitigation strategies. Our study was conducted to evaluate a novel approach that allowed near continuous CH(4) measurement from beef cattle confined in pens. The backward Lagrangian Stochastic (bLS) dispersion technique was used in conjunction with global position system (GPS) information from individual animals, to evaluate CH(4) emissions from pens of cattle. The dispersion technique was compared to estimates of CH(4) production using the SF(6) tracer technique. Sixty growing beef cattle were fed a diet containing 60% barley silage (dry matter basis) supplemented with either barley (Hordeum vulgare L.) grain or corn (Zea mays L.) distillers dried grains. The results show that daily CH(4) emissions were about 7% lower for the dispersion technique than for the tracer technique (185 vs. 199 g CH(4) animal(-1) d(-1)). The precision of the dispersion technique, relative to the SF(6) tracer technique, expressed by the Pearson coefficient was 0.76; the relative accuracy given by the concordance coefficient was 0.69. The bLS dispersion technique was able to detect differences (P < 0.05) due to diet and has the added advantage of measuring the pattern of CH(4) production during the 24-h period, with emissions ranging from 161 to 279 g CH(4) animal(-1) d(-1). Configuring the cattle as point sources resulted in more accurate CH(4) emissions than assuming a uniform area release from the pen surface. The results indicate that the bLS dispersion technique using cattle as point sources can be used to accurately measure enteric CH(4) from cattle and to evaluate the impact of dietary mitigation strategies.
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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) | 0.000 | 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 it