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Record W2412164410 · doi:10.1017/s0031182015000542

Host demographic predicts ectoparasite dynamics for a colonial host during pre-hibernation mating

2015· article· en· W2412164410 on OpenAlexaff
Quinn M. R. Webber, Zenon J. Czenze, Craig K. R. Willis

Bibliographic record

VenueParasitology · 2015
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicBat Biology and Ecology Studies
Canadian institutionsUniversity of Winnipeg
Fundersnot available
KeywordsBiologySwarming (honey bee)Myotis lucifugusZoologyParasite hostingHibernation (computing)EcologySocialityReproductionParasitismHost (biology)

Abstract

fetched live from OpenAlex

Parasite dynamics can be mediated by host behaviours such as sociality, and seasonal changes in aggregation may influence risk of parasite exposure. We used little brown bats (Myotis lucifugus) captured during the autumn mating/swarming period to test the hypothesis that seasonal and demographic-based variation in sociality affect ectoparasitism. We predicted that ectoparasitism would: (1) be higher for adult females and young of the year (YOY) than adult males because of female coloniality; (2) increase for adult males throughout swarming because of increasing contact with females; (3) decrease for adult females and YOY throughout swarming because of reduced coloniality and transmission of individual ectoparasites to males; (4) be similar for male and female YOY because vertical transmission from adult females should be similar. Ectoparasitism was lowest for adult males and increased for males during swarming, but some effects of demographic were unexpected. Contrary to our prediction, ectoparasitism increased for adult females throughout swarming and YOY males also hosted fewer ectoparasites compared with adult and YOY females. Interestingly, females in the best body condition had the highest parasite loads. Our results suggest that host energetic constraints associated with future reproduction affect pre-hibernation parasite dynamics in bats.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.012
Threshold uncertainty score0.667

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)0.0000.000

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 head0.020
GPT teacher head0.270
Teacher spread0.251 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations13
Published2015
Admission routes1
Has abstractyes

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