Large Soil Carbon Storage in Terrestrial Ecosystems of Canada
Bibliographic record
Abstract
Abstract Terrestrial ecosystems of Canada store a large amount of organic carbon (C) in soils, peats and plant materials, yet little is known about the C stock size and distributions, both spatially and in various C pools. As temperature rises, C is becoming available for disturbance, decomposition and eventual release into the atmosphere, which makes the quantification of C stocks in terrestrial ecosystems of Canada of high interest for the assessment of climate change impacts and conservation efforts. We used a large number of field measurements, multisource satellite, climate and topographic data and a machine learning algorithm to produce the first wall‐to‐wall estimates of C stocks and uncertainties in plants and soils of Canada at 250 m spatial resolution. Our findings show that above and belowground live biomass and detritus store a total of 21.1 Pg of carbon (Pg C). Whereas the Canadian soils store 306 (±147, 90% confidence interval) Pg organic C in the top 1 m, 98 Pg C of which are stored in peatlands, confirming that the soil organic C dominates terrestrial carbon stocks in Canada. We also find previously under‐reported large soil organic C stocks in forested peatlands on the boreal shields of Canada. Given that Canada is warming twice the global average rate and Canadian soils store approximately 20.4% of world soil C stocks in top 1 m, initiatives to understand their vulnerabilities to climate change and disturbance are indispensable not only for Canada but also for the global C cycle.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 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".