Assessment of the Sulfur Hexafluoride (SF<sub>6</sub>) Tracer Technique for Measuring Enteric Methane Emissions from Cattle
Bibliographic record
Abstract
A commonly used method of measuring enteric methane (CH4) emissions from ruminants is the SF6 tracer technique that measures respired and eructated CH4. However, within the animal, a small proportion of CH4 is produced post-ruminally and some of this may escape through the rectum. The comparison of emissions using a chamber technique that measures all enteric CH4 losses, and the SF6 tracer technique, could give some insight into the magnitude of post-ruminal emission. The objective of our study was to assess the precision and accuracy of the SF6 tracer technique against a chamber technique for cattle fed a range of diets. Using a repeated-measures design, eight beef heifers were offered a high grain or high forage diet for ad libitum or restricted (65% of ad libitum) feed intake to vary the site of digestion within the gastrointestinal tract (n = 24). The SF6 tracer technique underestimated CH4 emissions on average by 4% relative to the chamber technique. This difference was not significant (P > 0.05) and suggests low post-ruminal CH4 emissions. There was a trend for greater accuracy and precision of the SF6 tracer technique when used with cattle fed a high forage diet at a restricted level of intake. The high forage diet corresponds to the conditions of cattle grazing pasture, suggesting the SF6 tracer technique is most reliable for the grazing system.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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 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".