Global monthly CO<sub>2</sub> flux inversion with a focus over North America
Bibliographic record
Abstract
A nested inverse modelling system was developed for estimating carbon fluxes of 30 regions in North America and 20 regions for the rest of the globe. Monthly inverse modelling was conducted using CO2 concentration measurements of 3 yr (2001–2003) at 88 sites. Inversion results show that in 2003 the global carbon sink is -2.76 ± 0.55 Pg C. Oceans and lands are responsible for 88.5% and 11.5% of the sink, respectively. Northern lands are the largest sinks with North America contributing a sink of -0.97 ± 0.21 Pg C in 2003, of which Canada’s sink is -0.34 ± 0.14 Pg C.For Canada, the inverse results show a spatial pattern in agreement, for the most part, with a carbon source and sink distribution map previously derived through ecosystem modelling. However, discrepancies in the spatial pattern and in flux magnitude between these two estimates exist in certain regions. Numerical experiments with a full covariance matrix, with the consideration of the error structure of the a priori flux field based on meteorological variables among the 30 North America regions, resulted in a small but meaningful improvement in the inverted fluxes. Uncertainty reduction analysis suggests that new observation sites are still needed to further improve the inversion for these 30 regions in North America.
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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 | 0.000 | 0.001 |
| 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 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".