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Record W2122300590 · doi:10.1095/biolreprod.106.059550

Reproductive-Tactic-Specific Variation in Sperm Swimming Speeds in a Shell-Brooding Cichlid1

2007· article· en· W2122300590 on OpenAlexaff
John L. Fitzpatrick, Julie K. Desjardins, Nicole D. Milligan, Robert Montgomerie, Sigal Balshine

Bibliographic record

VenueBiology of Reproduction · 2007
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAnimal Behavior and Reproduction
Canadian institutionsQueen's UniversityMcMaster University
Fundersnot available
KeywordsSpermBiologySperm competitionZoologySpawn (biology)EcologyReproductive successAndrologyDemographyPopulationBotany

Abstract

fetched live from OpenAlex

Theory predicts that males experiencing elevated levels of sperm competition will invest more in gonads and produce faster-swimming sperm. Although there is ample evidence in support of the first prediction, few studies have examined sperm swimming speed in relation to sperm competition. In this study, we tested these predictions from sperm competition theory by examining sperm characteristics in Telmatochromis vittatus, a small shell-brooding cichlid fish endemic to Lake Tanganyika. Males exhibit four different reproductive tactics: pirate, territorial, satellite, and sneaker. Pirate males temporarily displace all other competing males from a shell nest, whereas sneaker males always release sperm in the presence of territorial and satellite males. Due to the fact that sneakers spawn in the presence of another male, sneakers face the highest levels of sperm competition and pirates the lowest, whereas satellites and territorials experience intermediate levels. In accordance with predictions, sperm from sneakers swam faster than sperm from males adopting the other reproductive tactics, whereas sperm from pirates was slowest. Interestingly, we were unable to detect any variation in sperm tail length among these reproductive tactics. Thus, sperm competition appears to have influenced sperm energetics in this species without having any influence on sperm size.

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)0.003
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.709
Threshold uncertainty score0.353

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.036
GPT teacher head0.268
Teacher spread0.232 · 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

Citations68
Published2007
Admission routes1
Has abstractyes

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