A new modelling framework to assess biogenic GHG emissions from reservoirs: The G-res tool
Why this work is in the frame
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Bibliographic record
Abstract
Human-made reservoirs are now recognized as potentially significant sources of greenhouse gases, comparable to other anthropogenic sources, yet efforts to estimate these reservoir emissions have been hampered by the complexity of the underlying processes and a lack of coherent budgeting approaches. Here we present a unique modelling framework, the G-res Tool, which was explicitly designed to estimate the net C footprint of reservoirs across the globe. The framework involves the development of statistically robust empirical models describing the four major emission pathways for carbon-based greenhouse gases (GHG) from reservoirs: diffusive CO2 and CH4 emissions, bubbling CH4 emissions from the reservoir surface, and CH4 emissions due to degassing downstream the reservoir, based on an extensive meta-analysis of published data from the past three decades. These empirical models allow the prediction of reservoir-specific emissions, how they may shift over time and account for naturally occurring GHG generating pathways in aquatic networks.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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