Does the abundance of ectoparasite in the nest affect nestling condition and fledging success?
Bibliographic record
Abstract
Nestlings of most passerine species face many stressors including early exposure to ectoparasites.Ectoparasites negatively impact the health of nestlings by feeding on their blood and feathers, leaving the nestlings in poor condition, and reducing their chance to fledge.European Starlings (Sturnus vulgaris) are cavity-nesting passerines; they nest in the holes of trees and artificial nest boxes which accumulate ectoparasites.Parents are known to line their nest with feathers to serve as a barrier to ectoparasites.Only one study on the ectoparasite community of European Starlings exists and it was done in Halifax, Nova Scotia (Fairn et al. 2014).My objectives were to 1) identify the abundance and types of ectoparasite in starling nests, 2) determine whether ectoparasite abundance reduces nestling condition and fledging success, and 3) determine whether the mass of feathers in the nest reduces ectoparasite abundance and to quantify the number of cigarette butts present in nests.This study was conducted in June 2020 on nine nests from the late broods of European Starlings.The number of ectoparasites per nest ranged from 8-31.The only ectoparasites found were adult hen fleas (Ceratophyllus gallinae).I found no relationship between ectoparasite abundance and a) mean nestling condition in the brood, b) proportion of nestlings that fledged and c) mass of feathers.These results suggest that nestlings were not affected by this particular prevalence of ectoparasites.It also suggests that feathers do not serve as a barrier which may instead be present in the nest to attract the opposite sex.Future studies should examine the effects of different ectoparasite prevalences on nestlings.
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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) | 0.001 | 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".