Temporal effects of selection logging on ground beetle communities in northern hardwood forests of eastern Canada
Bibliographic record
Abstract
We compared temporal changes in composition and abundance of ground beetles (Carabidae) among hardwood forest stands that underwent single-tree selection cutting 0.5-3 years previously, 15-20 years previously, and reference stands that are and will remain unmanaged in the park’s wilderness zone to determine impact of this silvicultural method on these important invertebrates. Short-term (0.5-3 year post-logging) effects included an increase in the number of forest generalist and open habitat species, a loss of large bodied species, and substantial (> 50%) reductions in activity densities of carabids; the latter effect correlated with significant reductions in leaf litter. Long-term (15-20 year post-logging) effects included an increase in forest generalist and open habitat species and increases in the activity densities of a few species that preferred the vegetation characteristics present in the 15-20 year treatment and absent from reference stands. Beetle communities in stands harvested 15-20 years previously contained activity densities and species composition similar to reference stands in the park, suggesting few long-term effects after the first rotation of this method. Introduced carabids were rare in our mostly forested study area. We recommend examining effects of a second rotation on carabid communities.
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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 | 0.001 | 0.001 |
| 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) | 0.001 | 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".