Modeling stability and resilience after slashburning across a sub-boreal to subalpine forest gradient in British Columbia
Bibliographic record
Abstract
Stability and resilience of conifer-dominated vegetation communities following clear-cutting and slashburning in central British Columbia, were modeled across gradients of resource availability, fire return interval (FRI), and fire severity. We hypothesized that high resource availability and long fire-free intervals would enhance stability, whereas high resource availability and short fire-free intervals would confer resilience. Fire weather indices and pre- and post-burn fuel loads were recorded and vegetation regrowth monitored for 5–11 years at 12 sites. Stepwise regression was used to model rates of revegetation, increases in vascular species richness, and pre- and post-burn similarity of species composition as a function of the environmental variables. Predicted stability for four sub-boreal to subalpine vegetation communities with contrasting resource availability and FRI corresponded closely to our hypotheses. Rates of revegetation were more strongly correlated with resource availability, whereas composition-based response variables were more strongly correlated with the FRI. Based on revegetation rates, all ecosystems were predicted to have equal resilience. However, based on vegetation composition, mesic sub-boreal ecosystems were predicted to be more resilient than mesic subalpine ecosystems because the degree of change in species composition was less sensitive to increasing burn severity. More slashburned sites with a broader range of burn severities are needed to verify these preliminary models.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".