Body size, not age, predicts parasite load in Clark’s Spiny Lizards (<i>Sceloporus clarkii</i>)
Bibliographic record
Abstract
Determining the factors that influence parasite load is a fundamental goal of parasitology. Body size often influences parasite load in reptiles, but it is unclear whether higher levels of parasitism are a result of greater surface area of individuals (a function of size) or of longer periods of exposure to parasites (a function of age). Using skeletochronology in a wild population of Clark’s Spiny Lizards (Sceloporus clarkii Baird and Girard, 1852), we tested the hypotheses that (i) larger individuals have higher parasite loads due to increased surface area available for colonization by parasites and their vectors and that (ii) older individuals have higher parasite loads because they have had longer exposure to parasites and their vectors. Males harboured more ectoparasites than females. Males and females differed in how body size influenced chigger (Acari: Trombiculidae) load; larger males harboured more chiggers than smaller males, but this was not the case in females. Age did not affect ectoparasite load in either sex. These results emphasize the importance of disentangling the effects of size and age in models of parasitism to gain a clearer understanding of intraspecific variation in parasite load.
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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) | 0.002 | 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".