Bayesian Hierarchical Modelling of Spruce Budworm Development
Bibliographic record
Abstract
The management of destructive forest pests such as the spruce budworm relies on accurate modelling of their development. Predicting the timing of specific events in the life cycle is crucial for pest control tactics and for modelling the landscape-scale dispersal of the insect. This thesis implements a Bayesian hierarchical thermal response model for the larval stages of the spruce budworm. The model was fitted to data collected from a laboratory rearing experiment on wild spruce budworm colonies collected from locations across Canada and on a fully lab-reared colony. The results were compared across developmental stages and geographic origins. The Bayesian model was implemented with the non-linear, temperature-dependent development rate curve outlined in Schoolfield et al. 1981 and the framework in Régnière et al. 2012 for individual variation and interval censored data. Posterior samples were obtained and a quadratic relationship was observed between developmental stage and an intercept parameter of the development curve. A second model was fitted to the data incorporating this structure. Distributions of development rate estimates at each rearing temperature were obtained from each posterior sample and it was observed that the lab-reared colony developed more quickly than the wild colonies. In future work, the posterior samples can be used to generate simulated populations for prediction, with uncertainty fully propagated throughout.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".