Response to logging by a limited but variable nest predator guild in the boreal forest
Bibliographic record
Abstract
Predation rates on the eggs and young of forest-nesting songbirds typically rise in association with anthropogenic fragmentation, but predator responses depend on the spatial scale of disturbance, context, and predator assemblages present. For landscapes that are naturally fragmented, such as the boreal forest, our understanding of nest predation patterns associated with harvest may be further confounded by an additive response of nest predators to the loss of forest cover and the extension of habitat edges. We examined predation rates on artificial nests across a range of values for landscape metrics reflecting natural and anthropogenic forest fragmentation during two summers in boreal forest stands of western Newfoundland, Canada. Nest predation by gray jays ( Perisoreus canadensis (L.)) increased significantly in logged areas, and gray jay abundance was positively linked to increasing amounts of logged edge; however, there was no response to the extent of natural openings suggesting that nest predation by jays was additive in the presence of harvest-created openings. In contrast, neither mammalian nest predators nor the unidentified predators (responsible for the largest proportion of nest losses) showed any association with the landscape fragmentation metrics assessed. Year effects shown by the unidentified nest predator category did coincide with a marked increase in small mammal and Newfoundland marten ( Martes americana (Turton) subsp. atrata (Bangs)) populations during our study. Thus, we were able to identify an additive predation response to logging, but also that the activities of predators may vary over space and time and, in turn, may variably influence the success of songbirds nesting in forests fragmented by logging.
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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) | 0.000 | 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".