The impact of spatially varying wetland source signatures on the atmospheric variability of <i>δ</i> D-CH <sub>4</sub>
Bibliographic record
Abstract
We present the first spatially resolved distribution of the [Formula: see text] signature of wetland methane emissions and assess its impact on atmospheric [Formula: see text]. The [Formula: see text] signature map is derived by relating [Formula: see text] of precipitation to measured [Formula: see text] of methane wetland emissions at a variety of wetland types and locations. This results in strong latitudinal variation in the wetland [Formula: see text] source signature. When [Formula: see text] is simulated in a global atmospheric model, little difference is found in global mean, inter-hemispheric difference and seasonal cycle if the spatially varying [Formula: see text] source signature distribution is used instead of a globally uniform value. This is because atmospheric [Formula: see text] is largely controlled by OH fractionation. However, we show that despite these small differences, using atmospheric records of [Formula: see text] to infer changes in the wetland emissions distribution requires the use of the more accurate spatially varying [Formula: see text] source signature. We find that models will only be sensitive to changes in emissions distribution if spatial information can be exploited through the spatially resolved source signatures. In addition, we also find that on a regional scale, at sites measuring excursions of [Formula: see text] from background levels, substantial differences are simulated in atmospheric [Formula: see text] if using spatially varying or uniform source signatures. This article is part of a discussion meeting issue 'Rising methane: is warming feeding warming? (part 1)'.
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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 | 0.000 | 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".