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Record W2042314880 · doi:10.1242/jeb.023820

SPERM FIND BIG EGGS BEST

2009· article· en· W2042314880 on OpenAlexaffabout
Erika J. Eliason

Bibliographic record

VenueJournal of Experimental Biology · 2009
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicReproductive biology and impacts on aquatic species
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsSpawn (biology)SpermBiologyOffspringZoologyEcologyMarine invertebratesSea urchinHuman fertilizationFisheryAnatomyPregnancyBotany

Abstract

fetched live from OpenAlex

There is widespread evidence that egg size has evolved through offspring and maternal fitness demands. Larger eggs typically bestow more nutrients, a larger birth weight and increased chance of survival on the offspring. Egg size is balanced with egg number to maximize maternal reproductive success. However, a new player has recently been added to the roster: sperm limitation, which occurs when there is an insufficient quantity of sperm to fertilize all of the available eggs. It has been suggested that in externally fertilizing animals, sperm limitation could also act as a selective force influencing egg size.Externally fertilizing animals, such as a sea urchins or fish, release their sperm and eggs into the surrounding environment with the hope that a sperm will bump into an egg and successfully fertilize it. It has recently been shown in three species of sea urchin that larger eggs need lower sperm concentrations in order to be successfully fertilized. Christopher MacFarlane from the University of East Anglia in the UK along with colleagues from Brandon University and the University of British Columbia in Canada sought to determine whether the same could be said for sockeye salmon, a fish with substantial natural variation in egg size where males and females spawn in much closer association compared with broadcast spawning marine invertebrates like sea urchins.In order to test whether larger sockeye salmon eggs were preferentially fertilized under conditions of sperm limitation, the authors collected sperm from 20 males and pooled eggs from 15 females. Dividing the pooled eggs into groups, the team added just enough sperm to each group of eggs to ensure fertilization success rates ranging from 20% to 80%. After allowing the eggs to incubate for 10 days, they measured the surface area of the fertilized and unfertilized eggs. Finally, the authors also included two treatments where all the eggs or none of the eggs were fertilized to account for possible changes in the size of the egg over the course of time or in response to fertilization. This also allowed the team to test whether there is a relationship between egg size and fertility when a surfeit of sperm is available.The team found that under sperm-limitation conditions, the eggs that were fertilized successfully had a significantly larger surface area (by 7%) than the unfertilized eggs, suggesting that increased egg size could evolve under conditions of sperm limitation.This study by MacFarlane and colleagues demonstrates that in addition to offspring and maternal fitness demands, egg size could also be influenced by sperm limitation in salmon. The next step in this line of research is to assess the importance of sperm limitation in fish in a natural setting. Though salmonids are generally considered to have high fertility in the environment, other species may not be so lucky. One thing seems to emerge clearly from this study: it's easier to hit a bigger target!

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation 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: none
Teacher disagreement score0.150
Threshold uncertainty score0.503

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.006
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0020.001
Scholarly communication0.0030.003
Open science0.0010.003
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.1500.053

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 head0.024
GPT teacher head0.332
Teacher spread0.308 · 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 source (direct Gemma or distilled Codex), 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

Citations0
Published2009
Admission routes2
Has abstractyes

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