Shift in feather mite distribution during the molt of passerines: the case of barn swallows (<i>Hirundo rustica</i>)
Bibliographic record
Abstract
Feather mites show a high diversity of distribution patterns on the wings of birds, but we are currently unable to make precise predictions about the distribution of mites on a given bird at a given time. This is especially intriguing because factors such as air turbulence, humidity, or temperature are already recognized as shaping feather mite distribution. We hypothesize that feather mites, rather than responding to single factors, respond at the same time to different constraints when deciding where to live. To test this hypothesis, we studied the distribution of mites along the wings of barn swallows ( Hirundo rustica L., 1758) in Europe before molting and in Africa during and after molt. Feather mite preferences shifted according to the stage of molt of the bird, with a pattern suggesting a clear compromise between being as close as possible to the non-molting distribution while avoiding the molt of the occupied feather and the early stages of growth of new feathers. Thus, we suggest that interacting factors, rather than single variables, must be studied to further advance the understanding of the distribution of feather mites on the wings of birds.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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".