The effect of natural and anthropogenic disturbances on the uncertainty of large-area forest growth forecasts
Bibliographic record
Abstract
This study aimed to estimate the contribution of disturbances to the uncertainty of forest growth forecasts in the Bas-Saint-Laurent region in Quebec, Canada. We focused on two major disturbances affecting that region: spruce budworm (SBW) outbreaks and harvest activities. Growth forecasts were carried out for a period of 100 years (2003–2103) using ARTEMIS-2009, a stochastic individual-based model. Using the Monte Carlo technique, we simulated four scenarios: a baseline; a harvest scenario; a SBW scenario; and a scenario including both harvest and SBW. Uncertainty estimation was performed using a bootstrap variance estimator that applies to the context of hybrid inference. The results revealed that the total variances increased over time. For the scenarios including SBW, the variances were three to six times greater than those in the scenarios without outbreaks. Harvesting did not greatly contribute to the total variance. We conclude that to reduce the uncertainty of large-area growth forecasts in the Bas-Saint-Laurent, considering SBW dynamics is a crucial issue.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".