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Record W2028967459 · doi:10.1139/z02-244

Milt quality, parasites, and immune function in dominant and subordinate Arctic charr

2003· article· en· W2028967459 on OpenAlexvenueno aff
Ståle Liljedal, Ivar Folstad

Bibliographic record

VenueCanadian Journal of Zoology · 2003
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAnimal Behavior and Reproduction
Canadian institutionsnot available
Fundersnot available
KeywordsBiologySpermZoologySperm competitionSalvelinusMatingReproductive successReproductionEcologyMate choiceMiltMating systemDemographyTroutFish <Actinopterygii>Genetics

Abstract

fetched live from OpenAlex

Within a species, different males may display different mating strategies. For example, some males may be selected to invest in attractiveness and mate guarding, whereas others are selected for increased sperm production and sneaky breeding. In systems with a hierarchical structure, dominant males are expected to adopt mate-guarding behaviour and subordinate males sneaky-breeding behaviour. In this study, we kept wild-caught and sexually ripe male Arctic charr (Salvelinus alpinus) in size-matched pairs and determined social rank from the number of aggressive encounters. After 4 days, subordinate males showed symptoms of stress, with higher blood glucose and erythrocyte levels than dominant males. There were no differences between dominant and subordinate males in parasite intensities or immune activity, measured as levels of granulocytes and lymphocytes in blood. Although subordinate males had smaller testes than dominant males, they still had a higher density of sperm cells and higher sperm numbers relative to the size of their testes. These results can be explained as indicating adaptation of subordinate males for reproduction in an unfavourable role, always exposed to sperm competition and out of synchrony with females' egg release. Our results suggest that rapid changes in social rank may affect ejaculate production.

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 score0.093
Threshold uncertainty score0.995

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 head0.023
GPT teacher head0.237
Teacher spread0.214 · 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

Citations44
Published2003
Admission routes1
Has abstractyes

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