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Record W2142752488 · doi:10.1139/z07-123

Patterns of parasitism and body size in red squirrels (Tamiasciurus hudsonicus)

2008· article· en· W2142752488 on OpenAlexafffundvenue
Jamieson C. Gorrell, Albrecht I. Schulte‐Hostedde

Bibliographic record

VenueCanadian Journal of Zoology · 2008
Typearticle
Languageen
FieldEnvironmental Science
TopicAnimal Ecology and Behavior Studies
Canadian institutionsLaurentian University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsParasitismBiologyFleaBiological dispersalSexual dimorphismZoologyEcologyPopulationHost (biology)Demography

Abstract

fetched live from OpenAlex

Parasites use their hosts for nutrition, shelter, and even dispersal; the latter can result in sex-biased parasite distribution. Host sex-biased parasitism has been well documented in vertebrates, including mammals, and males are often more parasitized than females. Male-biased parasitism is often attributed to sexual size dimorphism, with larger animals being more parasitized. Here, we used a natural population of red squirrels ( Tamiasciurus hudsonicus (Erxleben, 1777)), a species without sexual size dimorphism, to test for sex-biased parasitism in ectoparasites and intestinal helminth parasites. We also tested for size-dependent parasitism to determine the importance of body size on parasitism. We predicted that males would be more parasitized and that larger individuals would be more parasitized. As well, we predicted a male-biased flea distribution on male squirrels. Parasitism fluctuated over the course of 4 months, with flea infection peaking in August and helminth infection peaking in June. We found evidence of male-biased parasitism in helminth and ectoparasite infections. Flea infection was weakly correlated with body size in females but not in males, while no correlation was found between body size and helminth infection. Lastly, fleas had a female-biased population; however, male fleas were more likely to be found on male squirrels, and this could be to maximize dispersal to avoid inbreeding.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.155
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.013
GPT teacher head0.224
Teacher spread0.211 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations42
Published2008
Admission routes3
Has abstractyes

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