Variable retention harvest of white spruce stands and saproxylic beetle assemblages
Bibliographic record
Abstract
Dead wood dependent (saproxylic) insects have been identified as vulnerable to the effects of modern forestry practices. We examined the effects of variable retention harvesting on saproxylic beetle assemblages. Variable retention of living green trees seeks to leave more forest structure on the landscape with the goal of maintaining ecosystem function and biodiversity. Ninety flight-intercept traps were divided between recently dead natural snags and snags killed by girdling in three replicated forest stands with 10%, 20%, 50%, and 75% residual structure and in uncut control stands. Beetles were collected and identified during the second and third summers post harvest and grouped for analyses as (i) wood- and bark-borers, (ii) fungivores, and (iii) predators. Harvesting intensity explained a relatively small amount of the variability in the beetle assemblages. However, all groups responded strongly to coarse woody debris variables and especially to snag decay class during the third postharvest summer, suggesting that factors associated with coarse woody debris quality and quantity determine the initial responses and successional trajectories of saproxylic beetle assemblages. The main effects of variable retention on saproxylic assemblages are expected to be mediated through differences in amount of coarse woody debris expected to materialize with the death and decay of green trees left as residual elements.
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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 | 0.001 | 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".