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Fish sperm competition in hatcheries and between wild and hatchery origin fish in nature

2019· article· en· W2948498828 on OpenAlexaff
J. Beirão, Torvald B. Egeland, Craig F. Purchase, Jarle Tryti Nordeide

Bibliographic record

VenueTheriogenology · 2019
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicReproductive biology and impacts on aquatic species
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsHatcheryBiologySperm competitionSpermBroodstockCompetition (biology)CaptivityMatingStockingZoologyFisheryAquacultureEcologyFish <Actinopterygii>Genetics

Abstract

fetched live from OpenAlex

Males compete pre- and post-mating to fertilize the maximum number of eggs. In polyandry, sperm competition occurs when sperm from two or more males compete to fertilize eggs from a female. Here we review how sperm competition from hatchery origin fish can cause loss of genetic variability in fish populations kept in captivity and in wild populations. In fish hatchery practices, sperm competition occurs in mass spawners that release gametes in tanks, and in artificial fertilizations when pooled semen is used. In mass spawnings sperm competition is difficult to tease apart from pre-mating competition and other post-mating selective mechanisms, whereas, studies focused on the use of pooled semen in different fish species have shown a clear relationship between sperm motility parameters and precedence in fertilization. In both situations, sperm competition will result in a loss of genetic variability that accumulates over generations, but hatchery protocols can be adjusted to mitigate it. Another source of concern regarding sperm competition for hatchery produced fish is the spatial and temporal overlap in spawning with wild individuals, either via aquaculture escapees or purposeful stocking programs. This may result in sperm competition between hatchery origin and wild males and impact natural populations. Our review suggests that in order to give every adult selected as broodstock an equal opportunity to produce offspring in captivity, mass spawning and the use of pooled semen should be limited.

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)0.000
metaresearch head score (Gemma)0.001
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 score0.005
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.011
GPT teacher head0.250
Teacher spread0.239 · 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

Citations26
Published2019
Admission routes1
Has abstractyes

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