Forest fragmentation and body condition in wintering black-capped chickadees
Bibliographic record
Abstract
Winter represents a critical energy management challenge for northern birds. Subzero temperatures may persist almost without interruption for months, food supply steadily decreases, day length greatly limits time available for foraging, and they must endure long fasting at night. Body condition represents a potentially important concept to our understanding of how severe winters impact individual fitness and populations. Many researchers have addressed body condition in small wintering birds. However, studies published so far have rarely been placed into a landscape context, despite reported effects of landscape structure on the ecology of birds exposed to severe winters. We investigated whether forest cover affects body condition in a population of black-capped chickadees ( Poecile atricapillus (L., 1766)) during winter. We measured residual body mass, fat score, and rate of growth of induced feathers to assess body condition in 12 landscapes with forest cover ranging from 10% to 87%. Forest cover was not associated with any of these measures of body condition, suggesting that highly deforested and fragmented landscapes were not energetically more costly to chickadees. However, we may not rule out the possibility that this result was biased against by a higher emigration or mortality rate of individuals in poor body condition.
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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 | 0.001 | 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".