Genetic Variation in Growth Traits of <i>Scomberomorus</i> spp. Under Selective Breeding: A Case Study in Hainan Waters
Bibliographic record
Abstract
Scomberomorus spp. is one of the important economic fishes in China's offshore fisheries and aquaculture industries, but its wild population resources are under pressure from overfishing and environmental changes. Improve the growth performance of mackerels through selective breeding is expected to improve breeding efficiency and alleviate their dependence on wild resources. This study takes mackerel in Hainan as an example to analyze the genetic variation characteristics of growth traits (including body length, weight and growth rate) and the selection of breeding effects. The results show that the main growth trait of mackerel has a moderate level of heritability, and the selected breeding population exhibits faster growth rates and higher weight genetic gain relative to the natural population. At the same time, the genetic gain of different breeding generations shows a decreasing trend, showing that the impact of continuous selection on genetic variation needs attention. Case studies show that selective breeding strategies based on genetic parameter evaluation can effectively improve the growth traits of mackerels and provide practical basis for regional seawater fish breeding. This study provides scientific reference for the cultivation of mackerel species and the sustainable use of marine fishery resources in Hainan and other regions.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".