The fine‐scale population dynamics of spruce budworm: survival of early instars related to forest condition
Bibliographic record
Abstract
Abstract 1. A lagged, density‐dependent relationship between survival of early instars and host‐tree condition is revealed during outbreaks of spruce budworm, Choristoneura fumiferana Clem. Persistent damage to hosts leads to deterioration of the stand. 2. Resource limitation affects survival during early‐instar dispersal of spruce budworm. Impediments to distinguishing these events with estimates of survival were overcome with a simple model that describes the dispersal and survival processes. The model was used to analyse a recent 15‐year population series from Black Sturgeon Lake and two historical datasets from Green River, in Canada. 3. Defoliation‐induced damage to the trees resulted in increased losses of spring‐emerging larvae that are dispersing in search of feeding sites. Losses were further exacerbated by biotic factors such as maternal fecundity, rates of infection by the pathogen, Nosema fumiferanae , and by weather‐related effects on the foraging period. 4. Survival of early‐stage budworm larvae in persistent outbreaks declined and the likelihood of other density‐related factors such as rate of mortality from natural enemies increased. These results may reconcile outstanding differences in interpretation of the role of the forest resource in spruce budworm population dynamics and point to a common process linking the dynamics of other well‐known budworm species.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".