Outbreak regulation mechanisms play different roles at different stages of an Eastern spruce budworm (Choristoneura fumiferana) outbreak.
Bibliographic record
Abstract
Abstract Context: The stand level risk of defoliation and mortality due to the spruce budworm (Choristoneura fumiferana, SBW) is mainly linked to the host species content such that stands dominated by the primary host species, balsam fir (Abies balsamea), are considered the most vulnerable. At the landscape scale there is much less information but some work suggests that risk may decrease when landscapes are dominated by secondary hosts, such as black spruce(Picea mariana) or when landscapes are fragmented. Objectives: We aim to determine risk of defoliation onset and of tree mortality due to SBW at a landscape scale due to factors that have been associated with SBW defoliation at the stand scale. We hypothesise that outbreak onset will occur earlier in landscapes dominated by host species and that fragmentation and black spruce content will not affect onset but will be associated with subsequent lower mortality. Methods: We employed Cox hazard models to estimate the relative risk of different landscape configuration and species composition patterns on the onset and risk of mortality of a spruce budworm outbreak in Cote Nord, Quebec, Eastern Canada, using government data bases of forest tree species composition and of SBW defoliation across Québec. In particular, we measured the risks associated with primary host proportion, the relative amounts of secondary host and hardwood trees to fir trees, the structure of balsam fir in the landscape, and the structure of the forest overall. Results: Our results show that defoliation onset is more related to the configuration of the landscape than to the species composition while mortality is ultimately influenced by the tree species present. Defoliation onset risk is increased the most by a more complex configuration of fir patches in the landscape. Mortality risk on the other hand is unaffected by fir configuration, with the greatest change being a decrease in risk due to increased black spruce presence in the landscape. Conclusions: Maintaining black spruce abundance and forest connectivity across forested landscapes could reduce SBW related mortality and delay defoliation onset. Forestry management strategies should avoid clear cuts and the development of fir monocultures in the forest.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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 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".