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Record W4312128839 · doi:10.1002/jez.2677

Androgen and prolactin manipulation do not induce changes in immunocompetence measures in a fish with male parental care

2022· article· en· W4312128839 on OpenAlexafffund
A. A. P. Cunha, Shawn R. Garner, Erin M. Ingoldsby, Brian Dixon, Scott A. MacDougall‐Shackleton, Rosemary Knapp, Bryan D. Neff

Bibliographic record

VenueJournal of Experimental Zoology Part A Ecological and Integrative Physiology · 2022
Typearticle
Languageen
FieldImmunology and Microbiology
TopicAquaculture disease management and microbiota
Canadian institutionsUniversity of WaterlooMcMaster UniversityWestern University
FundersNatural Sciences and Engineering Research Council of CanadaConselho Nacional de Desenvolvimento Científico e Tecnológico
KeywordsImmunocompetenceProlactinPaternal careFish <Actinopterygii>AndrogenPsychologyBiologyZoologyInternal medicineEndocrinologyDevelopmental psychologyMedicineFisheryHormoneImmunologyPregnancyGeneticsImmune system

Abstract

fetched live from OpenAlex

Prolactin and 11-ketotestosterone (11-KT) are important reproductive hormones in fishes, which may also influence immunocompetence. The immunocompetence handicap hypothesis states that higher androgen concentrations that support secondary sex traits are traded off against a decrease in immune system function. To test the relationships between these hormones and immunocompetence, we experimentally manipulated 11-ketotestosterone and prolactin in the freshwater fish, bluegill (Lepomis macrochirus) during parental care using implants that contained either 11-KT, prolactin, or an inert control. We vaccinated individuals to stimulate the acquired immune response, then measured immunocompetence as the number of granulocytes, lymphocytes and monocytes, and the expression of interleukin 8 in each sample. We did not observe any significant differences in the immune measures among the hormone treatments. Our results indicate that in bluegill, there is no trade-off between androgens or prolactin and immunocompetence.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.790
Threshold uncertainty score0.812

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.019
GPT teacher head0.249
Teacher spread0.230 · 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 designBench or experimental
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

Citations1
Published2022
Admission routes2
Has abstractyes

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