Regression models for the estimation of carbon exchange in boreal forests
Bibliographic record
Abstract
Based on the measurements of the carbon dioxide fluxes at a few sites of the global FLUXNET network, we developed linear regression models for the estimation of the carbon budget of boreal forests. The model estimates satisfactorily agree with the measurement data obtained in the boreal forests of Canada (Manitoba, Thompson) and Russia (Krasnoyarsk krai, Zotino). The correlation coefficient between the calculations and measurements for a coniferous forests exceeds 0.9, with the annually mean model error in the budget estimate, as compared to the experimental results, not exceeding 50 gC/m 2 /yr. Using satellite and ground-based meteorological data, we calculated the monthly average carbon budget of the coniferous forests of Krasnoyarsk krai in 2001. Based on the satellite classification of the vegetation cover, we plotted monthly and annually average maps of the carbon budget that reflect the spatiotemporal distribution of the CO 2 uptake and emission rates. It is shown that the coniferous forests of Krasnoyarsk krai are mostly a carbon sink, uptaking as much as 300 gC/m 2 throughout the growing season.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".