Development and characterization of 248 novel microsatellite markers in turbot (Scophthalmus maximus)
Bibliographic record
Abstract
The turbot is a flatfish species of great relevance to marine aquaculture in Europe. Only a limited number of microsatellites have been isolated to date in this species. To increase the number of potentially useful mapping markers, we screened simple sequence repeat (SSR)--enriched genomic libraries obtained from several di-, tri-, and tetranucleotide tandem repeat motifs. A total of 248 new polymorphic microsatellites were successfully optimized. The efficiency of the protocol applied (6.4%) was higher than that in other studies of fish that used the same method. Dinucleotide and perfect microsatellites were predominant in this species; the (AC)n motif was the most frequent class of repeat. Polymorphism and structural properties at these loci, together with 30 variable loci previously reported in turbot, were evaluated in 6 wild individuals. The number of alleles per locus ranged from 2 to 10, with an average of 4.046. The microsatellite markers characterized in this study will contribute to the development of the turbot genetic map, which can be used for quantitative trait locus (QTL) identification, marker-assisted selection programs, and other applications to improve its culture.
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 | 0.001 |
| 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) | 0.000 | 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".