Nestling European Starlings (<i>Sturnus vulgaris</i>) adjust their begging calls in noise
Bibliographic record
Abstract
Anthropogenic noise, so common in cities, continues to increase with urbanisation. It adversely affects avian species that rely on acoustic forms of communication. The negative impacts are further exacerbated when parent-offspring communication is considered, especially in species where young are entirely dependent on the care of their parents. Our first objective was to study the effects that loud traffic noise had on nestling begging calls in European Starlings, Sturnus vulgaris, an urban-thriving species. For our second objective, we examined how this noise impacted parental provisioning and nestling condition. We found that the minimum frequency of the begging calls was higher in nestlings within experimental broods (exposed to traffic-noise playback) compared to that of nestlings in the control broods (exposed only to ambient noise). Also, nestlings in experimental broods continued to beg at a higher minimum frequency but with a narrowed bandwidth after the playback was stopped. Parental provisioning rates did not differ between control and experimental broods, nor did fledging success, although nestlings in the experimental group were in poorer condition. Our findings suggest that urban thrivers are affected by increasing traffic noise but have the phenotypic flexibility to adapt at a young age to maintain critical parent-offspring communication.
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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 | 0.000 | 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".