THE EFFECT OF HANDLING TIME ON INTERFERENCE AMONG HOUSE SPARROWS FORAGING AT DIFFERENT SEED DENSITIES
Bibliographic record
Abstract
Abstract Interference models of the ideal free distribution (IFD) assume competition among foraging animals causes intake rates to decline with increasing competitor density and that the strength of the decline influences forager distributions among food patches. However, the resulting distributions of animals may depend on which components of foraging success contribute to interference. We examined the effect of group size (1-13 birds) on the prey encounter rates, handling times, and foraging rates of house sparrows, Passer domesticus, feeding at three seed densities in a suburban backyard. House sparrows did not experience interference during search. Interference arose primarily from foraging time lost handling seeds. Foraging rates decreased with increasing seed density as a consequence of increased handling times. Also, birds experiencing significant increases in handling time with group size suffered most from interference. Our results suggest that animals adjust handling time to avoid costly aggressive interactions, indicating that handling time may be an important component of interference in some foraging systems. Future studies estimating interference should try to identify which components of foraging contribute to interference, paying particular attention to handling times for species that monitor and avoid competitors.
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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 | 0.002 |
| 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".