The ability of atmospheric data to resolve discrepancies in wetland methane estimates over North America
Bibliographic record
Abstract
Abstract. Existing estimates of methane fluxes from North American wetlands vary widely in both magnitude and distribution. In light of these disagreements, this study uses atmospheric methane observations from the US and Canada to analyze seven different bottom-up, wetland methane estimates reported in a recent model comparison project. We first use synthetic data to explore how well atmospheric observations can constrain wetland fluxes. We find that observation sites can identify an atmospheric pattern from Canadian wetlands but not reliably from US wetlands. The network can also identify the spatial distribution of fluxes in Canada at multi-province spatial scales. Based upon these results, we then use real data to evaluate the magnitude, temporal distribution, and spatial distribution of each model estimate. Most models overestimate the magnitude of fluxes across Canada. Most predict a seasonality that is too narrow, potentially indicating an over-sensitivity to air or soil temperatures. In addition, the LPJ-Bern model has a spatial distribution that is most consistent with atmospheric observations. Unlike most models, LPJ-Bern utilizes land cover maps, not just remote sensing inundation data, to estimate wetland coverage. A flux model with a constant spatial distribution outperforms most other existing flux estimates across Canada.
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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 | 0.002 | 0.005 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".