Host Migration and Size Do Not Influence the Prevalence of Most Chewing Lice (Phthiraptera: Amblycera and Ischnocera) on Shorebirds (Aves: Charadriiformes) across the World
Bibliographic record
Abstract
Patterns of prevalence in chewing lice (Phthiraptera) on wild birds are poorly known, as are the underlying factors that influence these patterns. Here, we analyze a data set consisting of published prevalence data of lice on shorebirds, as well as new prevalence data from shorebirds examined in Australia, Canada, China, Japan, and Sweden between 2007 and 2020. In total, prevalence data from 10 genera of lice from over 110 host species were included, including all major families of shorebirds. Using a generalized linear mixed model, we examine how the prevalence of lice of different genera varies between different sets of birds, focusing on two factors associated with migration (migration length and migration route). We found that host body size does not influence prevalence of lice in the Charadriiformes for any of the four most common and widely distributed louse genera (Actornithophilus, Austromenopon, Quadraceps, and Saemundssonia). Moreover, neither of the two migration variables showed any statistically significant correlations with prevalence, except for the genus Saemundssonia in which the prevalence of lice on short-distance migrants was significantly higher than on intermediate- and long-distance migrants. We also present 15 new records of chewing lice for China and 12 for Australia.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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".