Black-billed Magpies (Pica pica) adjust nest characteristics to adapt to urbanization in Hangzhou, China
Bibliographic record
Abstract
Although many bird species worldwide are colonizing urban environments, the ecological processes underlying the responses to urbanization remain unclear. Here we examined the responses of Black-billed Magpies ( Pica pica (L., 1758)) to urbanization across an urban–rural gradient by assessing nest locations, nest height, and available nest sites at six habitats (mountains, farmlands, riparians, urban parks, strips of street trees, building areas) in Hangzhou, China. In each habitat, we categorized used and available nest sites as urban (e.g., chimneys, antenna or cable poles, ledges, and open roofs on buildings) or natural (e.g., trees) nest sites. We found 147 magpie nests in five of the six habitats, and no nests in mountains. The use of urban nest sites by magpies differed significantly across habitats, and it increased significantly with the availability of urban nest sites along the urban gradients. Nest height of magpies differed significantly across habitats, and it increased significantly with urbanization intensity. The increase in nest height in urban environments can be attributed to the increases in human disturbance (the number of pedestrians). Our results indicate that magpies can adjust their nest characteristics in response to urbanization, and that nesting behavior shifts may aid them to adapt to urban systems.
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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) | 0.000 | 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".