Global monthly CO<sub>2</sub> flux inversion with a focus over North America
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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 | 0.002 |
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