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Record W2094457950 · doi:10.1139/f01-202

Fertilization dynamics in rainbow trout (<i>Oncorhynchus mykiss</i>): effect of male age, social experience, and sperm concentration and motility on in vitro fertilization

2002· article· en· W2094457950 on OpenAlexfundvenueno aff
N. R. Liley, Patrick Tamkee, R. S. Tsai, Drew J. Hoysak

Bibliographic record

VenueCanadian Journal of Fisheries and Aquatic Sciences · 2002
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicReproductive biology and impacts on aquatic species
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsMiltRainbow troutHuman fertilizationBiologySpermSperm motilityReproductionPopulationZoologyAnimal scienceAndrologyEcologyFisheryFish <Actinopterygii>BotanyAnatomyDemography

Abstract

fetched live from OpenAlex

We examine aspects of the fertilization dynamics of rainbow trout (Oncorhynchus mykiss) that may play a role in determining reproductive success of males of different age and status competing for spawning. There were no differences in the gonadosomatic indices and relative yields of milt of adult (3-year) and precocious (1-year) male rainbow trout collected from a wild population. The concentration of sperm in the milt of precocious males was higher than that of adult males. The duration of sperm motility was similar in the two groups of males and increased over the period of sampling. Interaction of a male with a nesting female caused an increase in milt yield, but did not affect sperm concentration, sperm motility, or fertilization rates. There was a sharp decline in fertilization rate 20 s or more after activation of the sperm or eggs by fresh water. Exposure to milt suspension for as little as 0.5 s resulted in fertilization of &lt;27% of eggs. The short gamete longevity and the speed with which fertilization occurs indicate that the timing and position of sperm release may play a critical role in determining the reproductive success of males in competition for spawning with a single female.

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)0.001
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 score0.021
Threshold uncertainty score0.293

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
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)0.0000.000

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 head0.017
GPT teacher head0.244
Teacher spread0.228 · 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

Citations102
Published2002
Admission routes2
Has abstractyes

Explore more

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