Wildfires offset the increasing but spatially heterogeneous Arctic–boreal CO2 uptake
Bibliographic record
Abstract
<span style="background-color:rgb(255,255,255);color:rgb(34,34,34);">The Arctic–Boreal Zone is rapidly warming, impacting its large soil carbon stocks. Here we use a new compilation of terrestrial ecosystem CO</span><sub>2</sub><span style="background-color:rgb(255,255,255);color:rgb(34,34,34);"> fluxes, geospatial datasets and random forest models to show that although the Arctic–Boreal Zone was overall an increasing terrestrial CO</span><sub>2</sub><span style="background-color:rgb(255,255,255);color:rgb(34,34,34);"> sink from 2001 to 2020 (mean ± standard deviation in net ecosystem exchange, −548 ± 140 Tg C yr</span><sup>−1</sup><span style="background-color:rgb(255,255,255);color:rgb(34,34,34);">; trend, −14 Tg C yr</span><sup>−1</sup><span style="background-color:rgb(255,255,255);color:rgb(34,34,34);">; </span><i>P</i><span style="background-color:rgb(255,255,255);color:rgb(34,34,34);"> < 0.001), more than 30% of the region was a net CO</span><sub>2</sub><span style="background-color:rgb(255,255,255);color:rgb(34,34,34);"> source. Tundra regions may have already started to function on average as CO</span><sub>2</sub><span style="background-color:rgb(255,255,255);color:rgb(34,34,34);"> sources, demonstrating a shift in carbon dynamics. When fire emissions are factored in, the increasing Arctic–Boreal Zone sink is no longer statistically significant (budget, −319 ± 140 Tg C yr</span><sup>−1</sup><span style="background-color:rgb(255,255,255);color:rgb(34,34,34);">; trend, −9 Tg C yr</span><sup>−1</sup><span style="background-color:rgb(255,255,255);color:rgb(34,34,34);">), and the permafrost region becomes CO</span><sub>2</sub><span style="background-color:rgb(255,255,255);color:rgb(34,34,34);"> neutral (budget, −24 ± 123 Tg C yr</span><sup>−1</sup><span style="background-color:rgb(255,255,255);color:rgb(34,34,34);">; trend, −3 Tg C yr</span><sup>−1</sup><span style="background-color:rgb(255,255,255);color:rgb(34,34,34);">), underscoring the importance of fire in this region.</span>
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 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".