Identifying insect outbreaks: a comparison of a blind-source separation method with host vs non-host analyses
Bibliographic record
Abstract
The identification of past insect outbreaks is often determined using a comparison of host/non-host tree ring growth chronologies. Yet this may be a problem when non-hosts are either affected by the outbreaking insect or when the growth of host and non-host trees does not respond similarly to the same climatic factors. We investigate the use of a blind source separation method to identify past outbreaks. This method, used in neurology and called independent components analysis (ICA), directly identifies disturbance patterns. We analysed the tree-ring data from papers dealing with insect outbreaks. These papers focus on western spruce budworm, pandora moth and Douglas-fir tussock moth outbreaks. We compared the results of the original analyses, conducted using the host/non-host approach, with results from ICA. We detected the outbreaks identified in the original papers. However, the start and end dates for the outbreaks were different in 75 per cent of the ICA analyses. On the other hand, we were able to detect growth reduction in non-host Ponderosa pine chronologies as well as increased growth during outbreak periods. Since conventional methods may be less robust when the growth of non-host trees is affected, the ICA may provide a powerful new method to identify outbreaks in such situation.
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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 | 0.011 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".