Improved estimates of net ecosystem exchanges in mega-countries using GOSAT and OCO-2 observations
Bibliographic record
Abstract
Accurate national terrestrial net ecosystem exchange estimates are crucial for the global stocktake. Net ecosystem exchange estimates from different inversion models vary greatly at national scale, and the relative impacts of prior fluxes and observations on these inversions remain unclear. Here we estimate the net ecosystem exchange of 51 land regions for the 2017-2019 period, focusing on the 10 largest countries, using prior fluxes from 12 terrestrial biosphere models and XCO2 retrievals from GOSAT and OCO-2 satellites as constraints. The average uncertainty reduction for the 10 countries increases from 37% with GOSAT and 45% with OCO-2 to 50% with combined observations, indicating a trend towards robust estimates. At finer spatial scales, even with combined observations, the uncertainty reduction is only 33%, i.e., the prior flux dominates the estimates. This finding underscores the critical importance of integrating multi-source observations and refining prior fluxes to improve the accuracy of carbon flux estimates. Choice of ecosystem model and input satellite data has a significant impact on modelled carbon dioxide flux and its associated uncertainty for large countries, according to atmospheric inversions using GOSAT and OCO-2 data.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".