Measurement of the isotopic signature of boreal wetland methane emissions
Bibliographic record
Abstract
The use of δ2H as well as δ13C methane isotope measurements will help improve regional and global source apportionment and understanding of reasons for methane’s continued and accelerating growth. However more data on regional variability in isotopic signatures of the main sources is required as well as regular measurements of both isotopes in methane in background air.Field campaigns across Canada in June 2022 and to northern Finland and Norway in August 2022 were carried out to collect air samples for methane δ13C and δ2H isotopic characterisation from boreal wetlands.In Finland and Norway a road campaign with continuous measurements of methane mole fraction was carried out from Södankyla, Finland to Aidejavri, Norway, with a generally decreasing gradient in methane from south to north and this has been compared with land cover maps. Air samples for isotopic analysis were collected at Södankyla, Kaamanen and Lompolojänkkä fens in Finland and Aidejavri and Suossjavri degrading palsa mires in Norway. In Canada δ2H and δ13C isotopic signatures were determined in methane emitted by wetlands in northern Ontario including at Fraserdale, and northern Saskatchewan (East Trout Lake).Overall the mean signatures of emissions from the boreal samples collected were -67‰ for δ13C and -320‰ for δ2H, but there was significant local variability when sampling air close to ground level. Aircraft campaigns would be a better way of identifying the integrated isotopic signature of regional wetland emissions (as demonstrated previously for δ13C signatures across northern European wetlands). Weekly sampling for methane δ13C and δ2H was started at Pallas Sammaltunturi in northern Finland in August 2022. These data will be used to identify the regional source signature of emissions.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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".