Traffic noise affects embryo mortality and nestling growth rates in captive zebra finches
Bibliographic record
Abstract
Over the past two decades, studies of songbird populations have detected decreases in the reproductive success of individuals living in urban areas. Anthropogenic noise is considered to be particularly detrimental, however the exact relationship between noise and reproductive success is still unclear because noise is often correlated with many other detrimental factors (e.g., predation, reduced territory quality). We used an experiment to specifically test the effects of urban noise on reproduction of captive zebra finches (Taeniopygia guttata). We found that latency to breed and the size of successfully fledged clutches were consistent between groups, however success of initial nesting attempts was reduced by traffic noise. Further, this reduced success leading to increased numbers of nesting attempts by birds in the noise condition was due to higher levels of embryo mortality in the traffic noise treatment group, which also suffered a lag in nestling growth rates during the first two weeks post-hatch. While parental baseline circulating corticosterone was not chronically affected by noise treatment, we identified some interaction effects whereby certain reproductive measures (laying latency and clutch size) were most strongly affected by treatment when mothers had higher levels of baseline corticosterone. These results indicate that traffic noise may reduce reproductive success through changes in parental behaviour, and that traffic noise may disproportionately affect chronically stressed individuals during reproduction. J. Exp. Zool. 323A: 722-730, 2015. © 2015 Wiley Periodicals, Inc.
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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".