Factors influencing the presence of parasitic trombiculids on red-backed voles (<i>Myodes gapperi</i>) in a temperate archipelago
Bibliographic record
Abstract
Parasites can influence host population health and dynamics and are often an overlooked aspect of the ecology of ecosystems. Understanding the impacts of ecological interactions between parasites and small mammals can provide insights into ecosystem dynamics. We live trapped small mammals within the Apostle Islands archipelago (2017–2020) and assessed factors influencing the presence of mites from the Trombiculidae family. Archipelagos provide unique research opportunities because in small mammal–parasitic trombiculid systems, they are essentially closed systems. We detected trombiculids on 47% of red-backed vole ( Myodes gapperi (Vigors, 1830)) individuals but rarely detected trombiculids on other species. We developed and ranked a set of a priori logistic regression models of trombiculid presence relative to habitat quality, host abundance, body condition, sex, and sexual maturity to identify factors significant in predicting trombiculid infection for M. gapperi. Parasitic trombiculids were more likely when M. gapperi abundance was high and body condition was poor; however, it is unknown whether trombiculids affect condition or if trombiculids are more likely to parasitize hosts in poor condition. The significance of host abundance may indicate density-dependent transmission. Our work suggests that host density and body condition are important factors influencing parasitism by trombiculids in M. gapperi populations.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".