Bibliographic record
Abstract
Micrometeorological techniques can be applied to estimate methane (CH4) emissions from ruminants and livestock manure using CH4 concentration measured within the internal surface boundary layer. The main advantage of these techniques is that they are non-intrusive, thereby eliminating the impact of the measurement set-up on the calculated CH4 emission. This review focuses on four micrometeorological techniques, namely, the integrated horizontal flux (IHF), flux gradient (FG), eddy covariance (EC) and the dispersion modelling using the backward Lagrangian stochastic method (BLS). Each technique has unique advantages and limitations when used for estimating enteric (ruminant) and manure CH4 emissions. The IHF technique may be theoretically simpler then the FG, EC or BLS techniques, but all require high-resolution instruments to measure concentration. The EC and BLS techniques also require a measurement of the wind statistics. This review discusses the appropriate use of these four micrometeorological techniques for estimating CH4 emissions in animal agriculture and the recent advances in measurement technology.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.003 |
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".