Using airborne technology to quantify and apportion emissions of CH4 and NH3 from feedlots
Bibliographic record
Abstract
A novel airborne approach using the latest technology in concentration measurements of methane (CH4) and ammonia (NH3), with quantum cascade laser gas analysers (QCLAs) and high-resolution wind, turbulence and other atmospheric parameters integrated into a low- and slow-flying modern airborne platform, was tested at a 17 000 head feedlot near Charlton, Victoria, Australia, in early 2015. Aircraft flights on 7 days aimed to define the lateral and vertical dimensions of the gas plume above and downwind of the feedlot and the gas concentrations within the plume, allowing emission rates of the target gases to be calculated. The airborne methodology, in the first instance, allowed the emissions to be qualitatively apportioned to individual rows of cattle pens, effluent ponds and manure piles. During each flight, independent measurements of emissions were conducted by ground-based inverse-dispersion and eddy covariance techniques, simultaneously. The aircraft measurements showed good agreement with earlier studies using more traditional approaches and the concurrent ground-based measurements. It is envisaged to use the aircraft technology for determining emissions from large-scale open grazing farms with low cattle densities. Our results suggested that this technique is able to quantify emissions from various sources within a feedlot (pens, manure piles and ponds), as well as the whole feedlot. Furthermore, the airborne technique enables tracing emissions for considerable distances downwind. In the current case, it was possible to detect elevated CH4 to at least 25 km and NH3 at least 7 km downwind of the feedlot.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".