A System Reanalysis of the Current Greenhouse Gases Budget of Terrestrial Ecosystems in Russia
Bibliographic record
Abstract
Abstract This study synthesizes the budgets of three greenhouse gases (GHG, namely CO 2 , CH 4 , N 2 O) for Russia over two decades (2000–2009 and 2010–2019) using bottom‐up and top‐down approaches, as part of the Regional Carbon Cycle Assessment and Processes, Phase 2 (RECCAP2). Published estimates of natural sources and sinks of these GHGs in Russia vary widely. Here, bottom‐up estimates are based on eddy covariance measurements, the Integrated Land Information System of Russia (ILIS‐LEA), field data, Dynamic Global Vegetation Models (DGVMs), and regional models. The bottom‐up approach estimated Net Ecosystem Exchange (NEE) at −0.64 ± 0.17 and −0.57 ± 0.14 Pg C yr −1 , for decades 2000–2009 and 2010–2019, respectively. Top‐down atmospheric inversions provide similar NEE carbon flux estimates with comparable uncertainties at −0.56 ± 0.26 and −0.73 ± 0.27 Pg C yr −1 for the two decades. Differences between these approaches arise from distinct flux components and structural assumptions. ILIS‐LEA indicates a slightly declining carbon sink in 2010–2019, driven by increased disturbances. In contrast, DGVMs suggest a stable carbon sink over both decades but they do not fully simulate the effects of disturbances and recovery. Top‐down inversions reveal an increasing CO 2 sink, suggesting with additional observed constraints on biomass carbon increment that soil and non‐forest biomes absorb more carbon than predicted by DGVMs and ILIS‐LEA models. A Bayesian averaging approach estimates natural ecosystems acting as a GHG sink with a land‐to‐atmosphere flux of −1.55 ± 0.91 and −1.47 ± 0.82 Pg CO 2 ‐eq. yr −1 . Accounting for both natural and anthropogenic emissions across the Russian territory shifts the net GHG balance to a source around 1.2 Pg CO 2 ‐eq. yr −1 .
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How this classification was reachedexpand
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 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".