Simulating the effects of past changes in climate, atmospheric composition, and fire disturbance on soil carbon in Canada's forests and wetlands
Bibliographic record
Abstract
Using the Integrated Terrestrial Ecosystem Carbon Cycle model (InTEC), six simulations with different input scenarios of climate, CO2, and nitrogen (N) deposition are conducted to study the changes of soil carbon (C) content in Canada's forests and wetlands during 1901–2000. Simulated total C stored in Canada's forest and wetland soils is 164.5 Pg C and accounts for about 7% of the global total of 2400 Pg C to the depth of 2 m, implying the significance of Canada's forest and wetland soils in the global terrestrial C cycle. Soils of Canadian forests and wetlands sequestrated 3.9 Pg C (2.6 Pg C in forests and 1.3 Pg C in wetlands) during 1901–2000 because of the integrated effects of climate, CO2fertilization, N deposition, and forest age factors. The changes of soil C content during 1901–2000 ranged spatially from −2 kg C m−2to 4 kg C m−2, depending on fire disturbance history, climate change pattern, and N deposition rates. Soil C increased by 2 to 4 kg C m−2in Eastern Hudson Plains, Eastern Middle Boreal Shield, Southern Boreal Shield, and Atlantic Maritime and decreased by more than 1 kg C m−2in Southern Boreal Plains. Simulations shows that climate influences growing conditions, growing season length, net N mineralization, and N fixation and therefore was the biggest driver of the increase in total soil C content during 1901–2000, followed by CO2fertilization and N deposition. The climate‐induced increase of soil C occurred mainly in the cool and wet period from the middle 1940s to the middle 1970s. Overall, an increase of 1°C in mean annual temperature induced heterotrophic respiration to increase by 62 Tg C a−1. In contrast to the century‐scale trend from 1901 to 2000, during the last two decades (1981–2000), CO2fertilization was the biggest driver of the increase in soil C, while climate change alone caused soil C to decrease.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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 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".