Some future implications for greenhouse gas mitigation policy frameworks: the example of methane oxidation by soils in a temperate oak woodland
Bibliographic record
Abstract
At the end of the 1990s, atmospheric concentrations of methane, a contributor to global warming, approached 1.8 parts per million by volume—nearly double pre-industrial levels. This is due not only to increasing emissions, but also to inhibition of natural sinks. One of these sinks occurs in soils. Two distinct groups of soil bacteria, the methanotrophs and the nitrifiers, are capable of methane oxidation. The highest rates of methane uptake occur in soils inhabited by methanotrophs, while the lowest rates are characteristic of nitrifying bacteria: ammonium fertilization tends to encourage dominance by nitrifiers. Short-term chamber experiments were carried out in a variety of different terrestrial environments in Victoria, British Columbia, Canada. Results were consistent with those obtained by other investigators for temperate forest sites elsewhere. Uptake rates of 0.059–0.082 mg·m−2 ·h−1 were measured at the Garry oak ( Quercus garryana) woodland, while the closed-canopy mixed forest (Acer macrophyllum and Pseudotsuga menziesii) had values in the 0.032–0.042 mg·m−2·h −1 range. Modified environments such as lawns had significantly lower uptake rates. An abandoned hayfield sampled for this study showed intermediate values. Other researchers have shown that it can take years or decades for environments to recover after reversion to low-nitrogen regimes, which is consistent with a long-term shift in bacterial community composition. Given that changes in land use affect soil processes which are intimately linked to atmospheric trace gas regimes, these issues will likely grow in importance over this century. Although current international legislation emphasizes the sequestration of atmospheric carbon dioxide in biomass, not all greenhouse gases follow this model. If mitigative policies are to be extended to other compounds such as methane and nitrous oxide, better understanding of non-sequestration sinks, e.g. soil uptake of CH4, and the processes regulating them is essential. More flexible “adaptive management” strategies are desirable, to accommodate changes in environmental conditions and scientific knowledge.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.005 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".