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Record W2139023739 · doi:10.1139/cjz-2014-0223

Host sex and parasitism in Red-winged Blackbirds (<i>Agelaius phoeniceus</i>): examining potential causes of infection biases in a sexually dimorphic species

2014· article· en· W2139023739 on OpenAlexvenueno aff
Tara E. Stewart, Loren Merrill

Bibliographic record

VenueCanadian Journal of Zoology · 2014
Typearticle
Languageen
FieldImmunology and Microbiology
TopicBird parasitology and diseases
Canadian institutionsnot available
Fundersnot available
KeywordsBiologyParasitismSexual dimorphismZoologyHost (biology)HelminthsEcology

Abstract

fetched live from OpenAlex

Sex biases in parasitism rates are widely reported in the literature. Among vertebrates in particular, males are more frequently parasitized than females. These sex-linked differences are often attributed to different investment strategies in current versus future reproduction at the ultimate level and to different levels of circulating androgens at the proximate level. But there are other factors that can influence parasitism rates that are often neglected. In this study, we examined multiple measures of parasitism in male and female Red-winged Blackbirds (Agelaius phoeniceus (L., 1766)) to determine whether sex biases occur and sought to identify physiological and ecological factors that may be shaping such differences. We assessed three groups of parasites (ectoparasites, intestinal parasites, blood parasites), one measure of immune function (bacteria-killing ability), and diet. We found that male-biased infections were common only for intestinal parasites, but could not be attributed to dietary or immunological differences. We also found that immune function was associated with an individual’s infection status, appearing elevated in males infected with helminths. Our results suggest that, for this species, sex biases in parasitism do occur but are not the norm. Furthermore, their existence may depend on the nature of the host–parasite relationship.

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)0.001
metaresearch head score (Gemma)0.001
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 score0.198
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
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 head0.019
GPT teacher head0.235
Teacher spread0.216 · 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

Citations5
Published2014
Admission routes1
Has abstractyes

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