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Record W3108847403 · doi:10.1186/s12862-020-01704-6

Reproductive consequences of an extra long-term sperm storage organ

2020· article· en· W3108847403 on OpenAlexafffund
Akashdeep Dhillon, Tabashir Chowdhury, Yolanda E. Morbey, Amanda J. Moehring

Bibliographic record

VenueBMC Evolutionary Biology · 2020
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAnimal Behavior and Reproduction
Canadian institutionsWestern University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsSpermathecaBiologySpermOffspringFemale sperm storageSperm competitionReproductive successZoologyEvolutionary biologyAndrologyGeneticsPopulationDemographyPregnancy

Abstract

fetched live from OpenAlex

BACKGROUND: Sperm storage plays a key role in the reproductive success of many sexually-reproducing organisms, and the capacity of long-term sperm storage varies across species. While there are theoretical explanations for why such variation exists, to date there are no controlled empirical tests of the reproductive consequences of additional long-term sperm storage. While Dipterans ancestrally have three long-term sperm organs, known as the spermathecae, Drosophila contain only two. RESULTS: We identified a candidate gene, which we call spermathreecae (sp3), in which a disruption cause the development of three functional spermathecae rather than the usual two in Drosophila. We used this disruption to test the reproductive consequences of having an additional long-term sperm storage organ. Compared to females with two spermathecae, females with three spermathecae store a greater total number of sperm and can produce offspring a greater length of time. However, they did not produce a greater total number of offspring. CONCLUSIONS: Thus, additional long-term sperm storage in insects may increase female fitness through extending the range of conditions where she produces offspring, or through increasing the quality of offspring via enhanced local sperm competition at fertilization.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.669
Threshold uncertainty score0.757

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 studies0.0000.001
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 head0.059
GPT teacher head0.271
Teacher spread0.212 · 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

Citations5
Published2020
Admission routes2
Has abstractyes

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