Influence of potential climate change on forest landscape dynamics of west-central Alberta
Bibliographic record
Abstract
Changes in climatic conditions may influence both forest biomass accumulation rates and natural disturbance regimes. While changes in biomass accumulation of forests under various climatic conditions have been described by yield equations, large uncertainties exist with regard to disturbance regimes. Under the doubling carbon dioxide scenario, global warming impacts have been predicted from simulation results of the first generation of coupled global climate model (CGCMI). The calculated fine fuel moisture code (FFMC) distribution from the simulation results showed a one-point increase compared with the distribution under current climate conditions. The impact of predicted changes in FFMC distributions on fire disturbance patterns, forest volume, and landscape structure was investigated by using the spatially explicit model for landscape dynamics (SEM-LAND). The simulation results showed increases in fire disturbance frequency and decreases in forest volume. The simulations also showed decreases in landscape fragmentation and landscape diversity, whereas total availability of core habitat for wildlife increased.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".