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Quality of testicular semen of the African catfish Clarias gariepinus (Burchell, 1822) and its relationship with fertilization and hatching success

2005· article· en· W2073476105 on OpenAlexaff
Nabil Mansour, Adel Ramoun, Franz Lahnsteiner

Bibliographic record

VenueAquaculture Research · 2005
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicReproductive biology and impacts on aquatic species
Canadian institutionsUniversity of Prince Edward Island
Fundersnot available
KeywordsSemenCatfishBiologyClarias gariepinusSemen qualityHuman fertilizationSpermHatchingSperm motilityAndrologyAnimal scienceAnatomyFisheryBotany

Abstract

fetched live from OpenAlex

Quality differences of testicular semen of the African catfish, Clarias gariepinus, and their influence on fertilization and hatching success were investigated. In accordance with an earlier study, two semen types of the African catfish were distinguished according to testicular maturity stage. Semen type I derived from males with white mature testes whereas type II semen derived from males with grey, partly mature testes. Semen volume, sperm cell concentration and seminal plasma pH was significantly higher in type I semen than in type II semen, while sperm motility was similar. Similar fertilization percentages were obtained with semen type I and semen type II. However, the hatching percentage was higher and the percentage of deformed hatched larvae was lower for type I semen. There were significant (P<0.01) positive correlations between sperm motility and fertilization percentage, seminal plasma pH and hatching percentage and a negative correlation between seminal plasma pH and percentage of deformed larvae. Therefore seminal plasma pH and sperm motility are useful to predict semen quality of the African catfish.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.080
GPT teacher head0.372
Teacher spread0.292 · 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

Citations16
Published2005
Admission routes1
Has abstractyes

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