The response of boreal forest songbird communities to fire and post-fire harvesting
Bibliographic record
Abstract
Post-fire timber harvesting (salvage logging) is becoming more prevalent as logging companies try to recover some of the economic losses caused by fire. Because salvaging is a relatively new practice and because of the common perception that burned areas are of little value to wildlife, few guidelines exist for salvaging operations. We surveyed birds in unburned and burned stands of jack pine (Pinus banksiana Lamb.), mixedwood, and trembling aspen (Populus tremuloides Michx.) to characterize the post-fire bird community in commercially important forest types. The effects of salvage logging were examined in mixedwood and jack pine. Using fixed-radius point counts, a total of 1430 individuals representing 51 species were detected during this study. Community analysis revealed that burned forests supported a distinct species assemblage of songbirds relative to unburned forests and that salvage logging significantly altered this community. An examination of guild composition showed that resident species, canopy and cavity nesters, and insectivores were the least likely to be detected in salvaged areas. Species less sensitive to salvage logging tended to be habitat generalists, omnivores, and species that nest on the ground or in shrubs. We suggest alternative management strategies that may help reduce the impact of salvage logging on the boreal forest songbird community.
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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 | 0.000 | 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".