Bibliographic record
Abstract
Carbon dioxide, methane and nitrous oxide emissions from agricultural sources have a significant role in the overall enhancement of the global greenhouse gas (GHG) effect. In research, measurements of GHG emissions are made to improve upon emission factors used in national inventories, identify and promote mitigation practices, and drive policy on GHG emissions in agriculture. These measurements are fundamental to the process of better management of GHG emissions. There is a variety of measurement techniques used in GHG research depending on the measurement environment and available resources. Techniques that use chambers or micrometeorological measurements are commonly employed for calculating emissions of GHG from point sources in agriculture, such as livestock and manure-holding facilities. This review examines these techniques, their limitations, and discusses methods to quantify their accuracy and precision. Emerging techniques like the use of dispersion models provide opportunities to directly determine emissions from whole farms. A few micrometeorological techniques (integrated horizontal flux and mass difference) are ideal for point sources such as manure storage facilities. For smaller sources, chambers are still recommended. In designing GHG emission studies, employing more than one technique when measuring GHG emissions is recommended, as often differences can exist due to technique. Ideally, a controlled release of the target gas, and its recovery, should also be conducted to evaluate techniques prior to their application. Although many techniques are often sensitive enough to quantify mitigation practices, i.e., the relative change in emissions, it is more difficult to determine “ true”emission factors as required for inventory work. It follows that the precision and accuracy of the techniques must accompany their application when estimating GHG emissions. Key words: Greenhouse gas, techniques, chambers, methane, agriculture, cattle
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".