The possible influence of intra-ruminal sulphur hexafluoride release rates on calculated methane emissions from cattle
Bibliographic record
Abstract
Estimates of methane (CH4) production from grazing animals are routinely made using the sulphur hexafluoride (SF6) tracer technique. While this technique is generally regarded as useful, some investigators report a higher variability in measurements when compared with calorimetry. The SF6 technique is a marker dilution method in which a known release rate of SF6 from an intra-ruminal permeation tube is used to calculate CH4 emissions from the ratio of SF6:CH4 in expired breath. The release rate of SF6 is unique for each tube, and although calculated CH4 emissions should be independent of SF6 release rate, an analysis of research conducted in New Zealand has suggested a possible influence of SF6 release rate upon calculated CH4 emissions. A modified cross-over design, with two groups of six steers given either one (2.878 mg SF6 d-1) or two (7.336 mg SF6 d-1) permeation tubes and offered either energy maintenance (M) or 2 × M levels of feed intake was undertaken to determine the effect of SF6 release rate and intake on calculated CH4 emissions. A high SF6 release rate elevated the calculated CH4 emission per day (P < 0.001) and per kg dry matter intake (kg DMI) by 19% (P < 0.001) irrespective of the level of intake. Release rate of SF6 can affect the calculated CH4 emissions from animals when employing the SF6 tracer technique. Key words: Methane, SF6 tracer technique, cattle, variability, feeding level
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".