Greenhouse gas emission factors associated with rewetting of organic soils
Why this work is in the frame
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Bibliographic record
Abstract
Drained organic soils are a significant source of greenhouse gas (GHG) emissions to the atmosphere. Rewetting these soils may reduce GHG emissions and could also create suitable conditions for return of the carbon (C) sink function characteristic of undrained organic soils. In this article we expand on the work relating to rewetted organic soils that was carried out for the 2014 Intergovernmental Panel on Climate Change (IPCC) Wetlands Supplement. We describe the methods and scientific approach used to derive the Tier 1 emission factors (the rate of emission per unit of activity) for the full suite of GHG and waterborne C fluxes associated with rewetting of organic soils. We recorded a total of 352 GHG and waterborne annual flux data points from an extensive literature search and these were disaggregated by flux type (i.e. CO2, CH4, N2O and DOC), climate zone and nutrient status. Our results showed fundamental differences between the GHG dynamics of drained and rewetted organic soils and, based on the 100 year global warming potential of each gas, indicated that rewetting of drained organic soils leads to: net annual removals of CO2 in the majority of organic soil classes; an increase in annual CH4 emissions; a decrease in N2O and DOC losses; and a lowering of net GHG emissions. Data published since the Wetlands Supplement (n = 58) generally support our derivations. Significant data gaps exist, particularly with regard to tropical organic soils, DOC and N2O. We propose that the uncertainty associated with our derivations could be significantly reduced by the development of country specific emission factors that could in turn be disaggregated by factors such as vegetation composition, water table level, time since rewetting and previous land use history.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 it