Stand-level response of breeding forest songbirds to multiple levels of partial-cut harvest in four boreal forest types
Bibliographic record
Abstract
We investigated whether impacts on boreal forest songbird communities in northwestern Alberta could be mitigated through a harvesting system that attempts to emulate the local natural disturbance regime. The EMEND (Ecosystem Management by Emulating Natural Disturbance) project is a multidisciplinary experiment to compare clearcuts and partial-retention cuts in four upland cover types with uncut forest and with experimentally burned stands. We studied breeding birds at EMEND between 1998 (pretreatment) and 2000, focusing on their responses to partial harvesting. Partial cuts were generally intermediate (and varied in a linear fashion) between clearcuts and undisturbed forest for community and species measures. Species that declined in abundance in partial cuts were typically dependent on shrubs and trees, whereas species that benefited were typically ground nesters. While partial cutting offered some advantages over clear-cutting in conserving short-term avian diversity, we suggest that low retention levels (i.e., 10%, 20%) cannot be justified from this perspective. The benefits that accrued in these treatments were relatively small, and species that declined or disappeared were typically characteristic of mature forest habitats. Higher retention levels (i.e., 50%, 75%) may conserve some species of concern, but the extent to which these treatments offer productivity advantages over lower residuals requires further study.
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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 | 0.000 | 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".