A flexible approach for predicting and mapping postfire wood borer attacks in black spruce and jack pine forests using the differenced normalized burn ratio (dNBR)
Bibliographic record
Abstract
Postfire salvage logging is used to reduce economic losses; however, burned trees are rapidly colonized by wood-boring insects, which reduce the merchantable value of the wood. This study aims to predict wood borer (Monochamus Megerle in Dejean, 1821) attacks after wildfire as a function of rapidly available variables such as tree basal area, stem diameter, and burn severity using the differenced normalized burn ratio (dNBR). In 2011, we sampled 60 black spruce (Picea mariana (Mill.) Britton, Sterns & Poggenb.) or jack pine (Pinus banksiana Lamb.) plots in five burns from 2010 in the Haute-Mauricie region of Quebec, Canada. A 50 cm bole section was debarked on seven trees in each plot to estimate wood borer attack density. Wood borer attacks were more abundant in black spruce than in jack pine. As a continuous variable, dNBR unveiled a quadratic effect of burn severity on attack density in black spruce, which was higher at moderate burn severity. In jack pine, the highest levels of attack density were found at high burn severity. Models produced in this article will help forest managers to better prioritize areas for salvage logging and thus reduce economic losses due to wood borer activity.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 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".