Modeling the soil consumption of atmospheric methane at the global scale
Bibliographic record
Abstract
A simple scheme for the soil consumption of atmospheric methane, based on an exact solution of the one‐dimensional diffusion‐reaction equation in the near‐surface soil layer, is described. The model includes a parameterization of biological oxidation that is sensitive to soil temperature, moisture content, and land cultivation fraction. The scheme was incorporated in the Canadian Land Surface Scheme (CLASS), with forcing provided by a 21‐a, global land meteorological data set, and was calibrated using multiyear field measurements. Application of the scheme on the global scale gives an annual mean sink strength of 28 Tg CH 4 a −1 , with an estimated uncertainty range of 9–47 Tg CH 4 a −1 . A strong seasonality is present at Northern Hemisphere high latitudes, with enhanced uptake during the summer months. Under the specified surface forcings, the oxidation parameterization is more sensitive to soil moisture than to temperature. Compared to the previous work of Ridgwell et al. (1999), our empirically based water stress parameterization reduces uptake more rapidly with decreasing soil moisture, resulting in a decrease of ∼50% in the potential global sink strength. Analysis of the geographical distribution of methane consumption shows that subtropical and dry tropical ecosystems account for over half of the global uptake.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".