Canada's Forests Are Shifting From a Recovery‐Driven Carbon Sink to a Disturbance‐Driven Carbon Source
Bibliographic record
Abstract
ABSTRACT Canada's terrestrial ecosystems are critical to the global carbon cycle and are responding to unprecedented climate change and wildfire disturbance. However, our understanding of Canada's historical (~1920—present) carbon cycle is incomplete. There are also no published physically coherent (i.e., those that respect conservation laws) wall‐to‐wall estimates of all major carbon pools and fluxes for Canada. Existing assessments vary in spatial scale and methodology, yielding notable differences in the magnitude of Canada's land carbon sink. Moreover, inversions and data‐driven estimates do not disentangle the relative influence of disturbance, CO 2 fertilization, or climate change on Canada's carbon cycle. Here, we synthesize information from the site to Canada‐wide scale with a land surface model and the most comprehensive wildfire and wood harvest estimates available to provide the first physically coherent wall‐to‐wall estimates of all major carbon pools and fluxes for Canada. Using factorial model runs, we show that Canada's terrestrial ecosystems have been a carbon sink since the mid‐20th‐century, due to wildfire and timber harvest before 1940. Since the early 2000s, wildfire disturbance has been driving Canadian forests towards becoming a carbon source. Continued increases in wildfire activity will further weaken, and may ultimately reverse, Canada's role as a carbon sink.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".