Landscape‐dependent distribution of northern forest birds in winter
Bibliographic record
Abstract
We evaluated the effects of landscape structure, along a broad gradient of deforestation (forest cover 8–88%, 500‐m radius), on the spatial distribution of forest birds exposed to winter climatic conditions, in Quebec, Canada. Concurrently, we conducted an experiment to determine if these effects would persist if an unlimited source of energy, provided by food‐supplementation, became available. We analyzed these effects at the population level, using count data of black‐capped chickadees Poecile atricapillus , but also at the community level, referring to species richness. In one of the two years of the study, before food‐supplementation began (November), “forest integrity” (a composite of forest cover and edge density) was positively associated with chickadee abundance and species richness. Each year, forest integrity was also positively associated with chickadee abundance and species richness in landscapes that were supplemented (December–February). However, in control landscapes, during the food‐supplementation period, chickadee abundance and species richness tended to decrease with an increase in forest integrity. We argue that the more forested control landscapes facilitated winter emigration of juveniles and transient birds. Conversely, our results further suggest that, in the highly deforested and fragmented control landscapes, birds became “gap‐locked” when rigorous winter climatic conditions exacerbated already existing movement constraints.
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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 | 0.001 | 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".