Source apportionment of methane using δ13C and δ2H 
Bibliographic record
Abstract
Use of both δ13C and δ2H measurements can be used to constrain methane sources. δ13C isotopes have been used to help identify the reasons for the continued growth in atmospheric methane, which since 2007 has coincided with a decline in δ13C. δ2H could offer a third dimension to help constrain the global methane budget, but its use has been limited because less data are available. There is a need for better identification of δ2H isotopic source signatures, and more long-term atmospheric data records.We present results of field campaigns carried out in a variety of source regions to characterise isotopic signatures and consider complexities in constraining source signatures for some categories. We also consider use of methane isotopic measurements at different scales for source partitioning.The isotopic signatures of urban emissions of methane have been characterised in London, Bucharest and Ho Chi Minh City. Methane sources in these cities are very different, with emissions being mostly from gas leaks in London, from wastewater and gas leaks in Bucharest, and from waste and traffic in Ho Chi Minh City.Measurements of cattle methane emissions in Jersey and Kenya show different isotopic signatures in methane from manure and eructation. Cattle diet, the age of manure and waste management practices cause variability in the isotopic signature of emitted methane.Wetland methane emissions from sites across Finland and Canada were collected in summer 2022. The Finnish boreal wetland methane isotopic signatures were δ2H -326 ± 19 ‰ and δ13C -68 ± 4 ‰, comparable with the results from Canada. Both δ2H and δ13C in methane from boreal wetlands tends to be more depleted in the heavier isotope than in tropical wetland methane emissions.Both δ13C and δ2H can be used in the UM-UKCA chemistry climate model which includes multiple methane tracers tagged by isotopic composition and source type. It is hoped that better characterisation of the regional variability in isotopic signatures of some sources will help improve the ability to model the global methane budget.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 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".