The impact of tree and stand characteristics on spruce beetle (Coleoptera: Scolytidae) induced mortality of white spruce in the Copper River Basin, Alaska
Bibliographic record
Abstract
I examined the relationships between individual and stand-level characteristics of white spruce, Picea glauca (Moench) Voss, and the incidence of spruce beetle, Dendroctonus rufipennis Kirby, induced mortality. The study region, in the Kennicott Valley of the Copper River Basin, Alaska, has contained an active spruce beetle epidemic since 1989. I investigated the relationship among the individual traits of host age, size (diameter at breast height, DBH), and growth rate (basal area increment, BAI) and mortality from the spruce beetle. I also examined the effects of stand density, mean DBH, and mean BAI on percent mortality within plots. Survival was higher for younger, smaller, and faster-growing trees. However, the effect of age is not significant when included in a logistic regression model examining the effect of individual host traits on host survival. Mortality increased with increasing DBH and decreasing BAI, and there was a significant interaction between DBH and BAI. While the proportion of individuals killed by the spruce beetle significantly differed between stands, I found no significant relationships between stand-level characteristics and mortality rate. This research suggests that the individual traits of host size and growth rate, as well as their interaction are the best predictors of susceptibility to spruce beetle-induced mortality in this system.
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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".