Autotetraploid Pacific oysters (<i>Crassostrea gigas</i>) obtained using normal diploid eggs: induction and impact on cytogenetic stability
Bibliographic record
Abstract
We describe two methods of producing viable and fertile autotetraploid Pacific oyster (Crassostrea gigas Thunberg) based on the use of normal-sized oocytes produced by normal diploid females. Our methods showed that the oocyte size is not a limiting factor for the success of the induction to autotetraploidy. These methods offer means of direct introgression of genetic progress from elite diploid lines to tetraploids used as broodstock, avoiding a triploid step with the risk of transferring undesirable traits from highly fecund triploids. High variability in the level of cytogenetic stability was found among the different tetraploid oysters tested, showing that induction method has an important impact on the long-term cytogenetic stability of the tetraploids. It appears that induction method based on the use of triploid females induces a greater cytogenetic instability among tetraploids so obtained, and this compared to tetraploids originating from the two methods described in our present study. As the aneuploidies and reversions observed in tetraploids can have serious consequences for the sustainability of tetraploid broodstock itself, as well as their triploid offspring, the two tetraploid induction methods described in the present work offer means to produce tetraploids with optimal cytogenetic, genetic, and zootechnical performances.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".