Climate change effects on net carbon exchange of a boreal aspen–hazelnut forest: estimates from the ecosystem model <i>ecosys</i>
Bibliographic record
Abstract
Summary Although boreal forests are currently sinks for atmospheric C, there is some concern that they may not remain so under hypothesized warming of the boreal climate. The ecosystem model ecosys was used to evaluate possible changes in ecosystem C exchange and accumulation under changes in atmospheric CO2 concentration (Ca) proposed in emissions scenario IS92a, and accompanying changes in air temperature and precipitation proposed by general circulation models running under IS92a. Ecosys was first tested under current climate by comparing modelled rates of C exchange and accumulation with those measured in a mixed aspen–hazelnut stand in central Saskatchewan. The model was then run with daily increments of Ca, temperature and precipitation, and differences in C exchange and accumulation between current and changing climates were evaluated. Model results indicated that over a 120‐y period, a mixed aspen–hazelnut stand currently accumulates about 14 kg C m−2. Under the hypothesized changes in climate this stand would accumulate an additional 8.5 kg C m−2, largely through higher rates of CO2 fixation and longer growing seasons under higher Ca and temperature. This additional accumulation would be entirely as aspen wood, while soil organic matter would change little. This accumulation would therefore be vulnerable to losses from fire and insects.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".