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Record W2075972554 · doi:10.1093/beheco/ari090

Male reproductive suppression in the cooperatively breeding fish Neolamprologus pulcher

2005· article· en· W2075972554 on OpenAlexaff
John L. Fitzpatrick, Julie K. Desjardins, Kelly A. Stiver, Robert Montgomerie, Sigal Balshine

Bibliographic record

VenueBehavioral Ecology · 2005
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAnimal Behavior and Reproduction
Canadian institutionsQueen's UniversityMcMaster University
Fundersnot available
KeywordsBiologyCichlidSperm competitionSpermReproductionReproductive successZoologyCooperative breedingFish <Actinopterygii>EcologySexual selectionPopulationFisheryDemographyGenetics

Abstract

fetched live from OpenAlex

In most cooperative breeders, dominants suppress the reproduction of subordinates. However, two previous studies of Neolamprologus pulcher, a cooperatively breeding cichlid fish, have suggested that socially subordinate helper males sneak fertilizations from dominant breeding males. If such sneaking does occur, both theoretical work and empirical studies of other fish species suggest that sperm competition will select for increased reproductive investment by sneaker males, relative to more dominant males. To address these issues, we quantified gonadal investment and sperm characteristics of 41 N. pulcher male breeders and 62 male helpers from 55 groups in Lake Tanganyika. Gonadal investment followed patterns consistent with reproductive suppression, with breeders having considerably larger testes masses than helpers. Breeders also had faster and longer swimming sperm and a higher percentage of motile sperm compared to helpers. However, sperm characteristics of large helpers were similar to those of breeders, but these same helpers had lower testes masses. Thus, large helpers had sperm that were physiologically equivalent to that of breeders, but their relatively small gonads imply that they were reproductively suppressed.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.506
Threshold uncertainty score1.000

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)0.0010.000

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 head0.048
GPT teacher head0.283
Teacher spread0.235 · 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.

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

Citations80
Published2005
Admission routes1
Has abstractyes

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