Verifying methane emission estimates from agricultural regions in Eastern Ontario using TROPOMI product
Bibliographic record
Abstract
The two main sources of CH4 from the agricultural sector are enteric fermentation and manure management systems. Canada uses the IPCC Tier-II methodology to estimate CH4 for its national inventory report of GHG emissions to UNFCCC, which is based on a bottom-up approach using activity data and emission factors obtained through site level experimental measurements. However, because of the presence of wetlands in some agricultural regions, it has been challenging to obtain accurate CH4 emission estimates at a regional scale. This study explores the usefulness of S5P methane product for verifying methane emission estimates in eastern Ontario agricultural land. We investigated the spatiotemporal variability of total column methane mixing ratio, as well as other detailed data layers in the TROPOMI product, such as averaging kernels and a prior profiles. The spatial temporal patterns of wetland methane emission derived from the global WetCHARTs dataset, and a prior knowledge of livestock distribution in the region, are used to interpret S5P methane product. Results showed that TROPOMI methane product provides great spatiotemporal coverage that can be used to verify CH4 emissions from agricultural landscape. This will be useful to reduce methane estimation uncertainties at the regional and national scales.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 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".