Vertical distribution of three longhorned beetle species (Coleoptera: Cerambycidae) in burned trees of the boreal forest
Bibliographic record
Abstract
This study aimed to characterize the vertical distribution of longhorned beetle larvae in burned trees of the eastern Canadian boreal forest. Black spruce (Picea mariana (Mill.) Britton, Sterns & Poggenb.) and jack pine (Pinus banksiana Lamb.) trees burned at three severity levels were cut, and 30 cm boles were collected from the ground up to a height of 9.45 m. Boles were debarked and dissected to collect insect larvae. Results show that the three most abundant longhorned beetle species were vertically segregated among burned jack pine and black spruce trees, but the section having the highest timber value was heavily infested by woodborer larvae. Larval density distribution of Monochamus scutellatus scutellatus (Say) and of Acmaeops proteus proteus (Kirby) could be linked with bark thickness, which also depends on fire severity. Lightly burned stands of black spruce were the most heavily infested and should be salvaged only if they are easily accessible and can thus be rapidly harvested and processed at the mill. More severely burned stands should be salvaged later as they will be less affected by woodborers, as should jack pine, which is lightly infested compared with black spruce. The ecological role of stumps should be further investigated because they could still have an ecological value after salvage logging as Arhopalus foveicollis (Haldeman) uses them specifically.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 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".