Large‐scale Input Matching by Urban Feral Pigeons (<i>Columba livia</i>)
Bibliographic record
Abstract
Abstract Ideal Free Distribution (IFD) theory predicts the number of animals choosing habitats of differing quality. Most experimental tests of the IFD have been conducted at small spatial scales (i.e. smaller than maximum daily movement of animals) by comparing the number of animals foraging at adjacent food patches of different quality. Urban pigeons (Columba livia) feed in large, open aggregations, and can distribute according to predictions of the IFD at alternative food patches. In this study, we test IFD predictions over a much larger spatial scale by comparing the abundance of urban feral pigeons at four sites spread over the city centre of Montréal, Québec, Canada, to the amount of anthropogenically provided food in each site. We found that the pigeons’ distribution among the four sites qualitatively matched that of resources available at these sites. After controlling for the effect of stochastic variation in food resources, two pair‐wise comparisons between sites indicated undermatching, one indicated matching and three indicated overmatching of consumers to resources. These results suggest that the pigeons inhabiting the downtown area of Montréal may behave as a single population that distributes qualitatively among foraging sites in proportion to the quantity of food offered, and that deviations from expectations cannot be attributed simply to stochastic variation in the food levels at the sites.
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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) | 0.002 | 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".