Salvage Logging, Edge Effects, and Carabid Beetles: Connections to Conservation and Sustainable Forest Management
Bibliographic record
Abstract
We used pitfall traps to study the effects of fire and salvage logging on distribution of carabid beetles over a forest disturbance gradient ranging from salvaged (naturally burned and subsequently harvested) to unsalvaged (naturally burned and left standing). Significantly more carabids were caught in the salvaged forest and the overall catch decreased steadily through the edge and into the unsalvaged forest. We also noted a strong negative correlation between carabid abundance and percent vegetation cover. Beetle diversity as measured through rarefaction was significantly greater at the edge relative to both the unsalvaged and salvaged forest. This stand level study suggests that the amount of edge habitat created by salvage logging has significant implications for recovery of epigaeic beetle assemblages in burned forests by inflating the abundance of “open habitat” species in the initial communities. Carabid beetle responses to salvage logging can differ from responses to harvesting in unburned boreal forest suggesting that management of postfire forests requires special consideration.
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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".