Molecular Method for Sex Identification of Half-Smooth Tongue Sole (Cynoglossus semilaevis) Using a Novel Sex-Linked Microsatellite Marker
Bibliographic record
Abstract
Half-smooth tongue sole (Cynoglossus semilaevis) is one of the most important flatfish species for aquaculture in China. To produce a monosex population, we attempted to develop a marker-assisted sex control technique in this sexually size dimorphic fish. In this study, we identified a co-dominant sex-linked marker (i.e., CyseSLM) by screening genomic microsatellites and further developed a novel molecular method for sex identification in the tongue sole. CyseSLM has a sequence similarity of 73%-75% with stickleback, medaka, Fugu and Tetraodon. At this locus, two alleles (i.e., A244 and A234) were amplified from 119 tongue sole individuals with primer pairs CyseSLM-F1 and CyseSLM-R. Allele A244 was present in all individuals, while allele A234 (female-associated allele, FAA) was mostly present in females with exceptions in four male individuals. Compared with the sequence of A244, A234 has a 10-bp deletion and 28 SNPs. A specific primer (CyseSLM-F2) was then designed based on the A234 sequence, which amplified a 204 bp fragment in all females and four males with primer CyseSLM-R. A time-efficient multiplex PCR program was developed using primers CyseSLM-F2, CyseSLM-R and the newly designed primer CyseSLM-F3. The multiplex PCR products with co-dominant pattern could be detected by agarose gel electrophoresis, which accurately identified the genetic sex of the tongue sole. Therefore, we have developed a rapid and reliable method for sex identification in tongue sole with a newly identified sex-linked microsatellite marker.
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 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 | 0.000 |
| 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 | 0.001 |
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".