Age, sex, and temperature shape off-territory feeder use in black-capped chickadees
Bibliographic record
Abstract
), to assess the causes and consequences of within- and among-individual differences in chickadee space use. Individuals that used feeders in addition to their most-visited "core feeder" were defined as engaging in off-territory feeder use. We found that females were more likely than males to visit off-territory feeders as ambient temperatures decrease and energetic demands increase. This may be due to sex-related differences in dominance, where males which are the dominant sex in chickadees, have priority access to feeders, while females must increase their foraging areas to meet higher energetic demand. We also found that independent of temperature, adult males were less likely than juvenile males and both juvenile and adult females to forage off-territory. We suggest that this may be due to age-specific benefits of space use in males, where un-paired juvenile males may increase feeder exploration to gain information about potential mates. Finally, we found that chickadees that had a higher propensity to visit off-territory feeders were less likely to survive to the next fall. Overall, our results suggest that dominance hierarchies and individual energetics impact within- and among-individual variation in off-territory feeder use. We provide suggestions for future studies to further investigate fitness-related consequences of within- and among-individual variation in space use.
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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 | 0.000 | 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".