The consumption of atmospheric methane by soil in a simulated future climate
Bibliographic record
Abstract
Abstract. A recently developed model for the consumption of atmospheric methane by soil (Curry, 2007) is used to investigate the global magnitude and distribution of methane uptake in a simulated future climate. In addition to solving the one-dimensional diffusion-reaction equation, the model includes a parameterization of biological CH4 oxidation that is sensitive to soil temperature and moisture content, along with specified reduction factors for land cultivation and wetland fractional coverage. Under the SRES emission scenario A1B, the model projects an 8% increase in the global annual mean CH4 soil sink by 2100, over and above the 15% increase expected from increased CH4 concentration alone. While the largest absolute increases occur in cool temperate and subtropical forest ecosystems, the largest relative increases in consumption (>40%) are seen in the boreal forest, tundra and polar desert environments of the high northern latitudes. Methane uptake at mid- to high northern latitudes increases year-round in 2100, with a 68% increase over present-day values in June. This increase is primarily due to enhanced soil diffusivity resulting from lower soil moisture produced by increased evaporation and reduced snow cover. At lower latitudes, uptake is enhanced mainly by elevated soil temperatures and/or reduced soil moisture stress, with the dominant influence determined by the local climate.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".