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Record W1994456240 · doi:10.1139/z05-062

Trade-offs between molt and immune activity in two populations of house sparrows (<i>Passer domesticus</i>)

2005· article· en· W1994456240 on OpenAlexvenueno aff
Lynn B. Martin

Bibliographic record

VenueCanadian Journal of Zoology · 2005
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAnimal Behavior and Reproduction
Canadian institutionsnot available
Fundersnot available
KeywordsBiologyImmune systemPopulationTrade-offZoologyFeatherPasserEcologyHouse miceCaptivityImmunologySparrowDemography

Abstract

fetched live from OpenAlex

Molt and immune defense are critical activities in which all birds must invest. Because each is costly, wild passerines may have to decrease their investment in one activity if they are to increase investments to others. Here, I studied such molt–immune trade-offs in one neotropical and one north-temperate population of house sparrows (Passer domesticus (L., 1758)). I included two populations in my study to investigate if molt–immune trade-offs in this species are phenotypically plastic or fixed. I expected that if they were fixed, then immune activity, molt, and immune–molt trade-offs would be distinct between populations after they were kept in captivity for 1 year under similar conditions. I found evidence for molt–immune trade-offs in house sparrows. Feather growth was inversely related to cutaneous immune activity to phytohemagglutinin (PHA). Furthermore, feather growth 3 weeks post immune challenge was lower in immune-challenged birds relative to saline-injected controls. However, there was no effect of population of origin on these patterns, or the rate of molt or PHA response at this time of year in each population. Thus, while house sparrows probably do face trade-offs between molt and immune activity in the wild, any variation in these trade-offs between populations are likely plastic responses to different environments.

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 score0.178
Threshold uncertainty score0.993

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 head0.034
GPT teacher head0.262
Teacher spread0.228 · 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

Citations74
Published2005
Admission routes1
Has abstractyes

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