The effect of fires on susceptibility of subalpine forests to a 19th century spruce beetle outbreak in western Colorado
Bibliographic record
Abstract
In the subalpine forests of the Colorado Rocky Mountains, research on disturbances that have occurred over the past several decades has shown that prior occurrence of disturbances can alter the extent and severity of subsequent disturbances. In the current study, we consider how fire history affected stand susceptibility to a mid-19th century spruce beetle (Dendroctonus rufipennis Kirby 1837) outbreak. Twenty-one sites were randomly located in an Engelmann spruce – subalpine fir (Picea engelmannii Parry ex Engelm. – Abies lasiocarpa (Hook.) Nutt.) forest across ~2000 km2 of the Grand Mesa area, Colorado. At each site, dendrochronological methods were used to reconstruct the history of severe fires and beetle outbreak. Stand-origin dates were estimated by collecting increment cores from 20–27 of the largest trees at each sample site. The beetle outbreak was reconstructed based on coincident releases among nonhost trees that survived the outbreak. Forest stands originated following severe fires in ca. 1790, ca. 1740, and ca. 1700. The 1840's outbreak affected 67% of these stands. Stands that initiated following the ca. 1790 fire were less susceptible to the outbreak than older stands. These findings indicate that stand-replacing fires have mitigated susceptibility to outbreaks of spruce beetles not only during recent outbreaks, but also over the past centuries.
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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 | 0.001 | 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) | 0.000 | 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".