Carbon under Canadian forests- why soils matter
Bibliographic record
Abstract
Forests occupy about 40% of Canada, with managed forests totalling 225 million hectares. While Canadian forests have historically acted as an essential carbon sink for Canada, intensifying disturbances have drastically decreased the carbon sink provided by trees. However, most carbon is found belowground in Canadian forests, with forest floors and mineral soils containing more than three times the amount of carbon stored in trees. About 28,800 million tonnes of carbon are sequestered in mineral soils of Canadian forests alone, corresponding to 10% of stocks found in forest soils globally. Even slight variations in these extensive carbon stocks can have a profound impact not only on the carbon balance of Canadian forests but also on the global carbon cycle. Despite their importance, there is still great uncertainty about the mechanisms controlling soil carbon persistence in Canadian forests.Our research project aims to address this major knowledge gap by quantifying soil carbon formation and persistence across the major forested ecozones of Canada. We have established a nationwide network of experimental sites to compare key soil types under different tree species representative of Canadian-managed forests. We will measure the decadal sensitivity of soil carbon to environmental shifts, including global change, harvesting and fire. We will clarify the linkages between carbon persistence and soil biodiversity. Overall, this research project will establish the foundational scientific knowledge required to improve current predictions of soil carbon response to environmental shifts in Canadian forests. This will, in turn, allow for a meaningful inclusion of forest soil carbon in Canadian climate policies, including global commitments under the United Nations Framework Convention on Climate Change.
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.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".