Influence of Climate Change, Fire, Insect and Harvest on Carbon Dynamics for Jack Pine in Central Canada: Simulation Approach with the EFIMOD Model
Bibliographic record
Abstract
Changes in disturbance regimes, both natural and anthropogenic, will be an important mechanism by which northern ecosystems respond to climate change, and are an important feedback mechanism in changing ecosystem processes. A forest ecosystem model, EFIMOD, was applied to the jack pine (Pinus banksiana Lamb.) stands in Central Canada to simulate the influence of climate warming, fire, insects and harvesting on jack pine productivity and pools of soil carbon (C). For the climate change simulations, temperature and precipitation prediction data from three General Circu- lationModels(GCMs)wereused:theCanadianClimateCentreforModellingandAnalysis,CGCM2; the UK Hadley Centre, HadCM3; and the Australian CSIRO Mark 2 GCM. In all three of these scen- arios, whole trees biomass increased, whereas the soil organic C pool consistently decreased. The CSIRO and CGCM scenarios had the strongest affect on biomass, soil, and ecosystem processes (net primary productivity, soil respiration, and net ecosystem productivity). Assessing the net ecosystem productivity over 150 years for jack pine stands shows a C sink under different disturbance regimes: 'nodisturbances>harvest-fire>twofires>insectattack>harvesting>fire-harvest';andnetcarbon source under a four fires scenario. The disturbance frequency was found to have a strong and lasting influence onthedynamicsoftheCstocks,andadirecteffectonCsource/sinkrelationship.Soilnitrogen content, which reflect the productivity potential, modify the effect of climate change and disturbances.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".