Depredation of artificial bird nests along roads, rivers, and lakes in a boreal Balsam Fir, Abies balsamea, forest
Bibliographic record
Abstract
Depredation of artificial bird nests along roads, rivers, and lakes in a boreal Balsam Fir, Abies balsamea, forest.Canadian Field-Naturalist 114(1): 83-88.Predation of nests of forest birds increases near edges in agricultural landscapes and this edge effect has been extrapolated to other ecosystems, including forests managed for timber harvesting.However, current literature suggests that ecological processes occurring in fragmented forest landscapes in relation to bird nest predation differ from those occurring in agricultural landscapes, as most studies conducted in forested ecosystems have found no edge effects.Nevertheless, in both landscapes, few studies have compared predation effects along different types of edges.In a boreal Balsam Fir (Abies balsamea) forest, we evaluated predation risk of artificial bird nests in five forest-highway ecotones, five forest-logging road (13-m-wide) ecotones, five riparian strips along rivers, and five riparian strips along lakes.We used ground and arboreal (5-m-high) artificial nests in which we placed two Common Quail (Coturnix coturnix) eggs and one plasticine egg.Predation was highest in forest-highway ecotones, intermediate in riparian forest strips along lakes and forest-logging road ecotones, and lowest in riparian forest strips along rivers.The American Crow (Corvus brachyrhynchos), a generalist species, was an important predator along highway and lake ecotones, but was nearly absent along logging road and river ecotones.The Red Squirrel (Tamiasciurus hudsonicus), a forest-specialist species, co-dominated along highway and lake ecotones.Our results suggest that in this ecosystem, nest predation along edges is probably not problematic, unless human activities provide food to generalist predators.More studies are required to evaluate how new food sources resulting from human activities contribute to the establishment and sustainment of populations of generalist predators in forested areas where agriculture is absent.
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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".