Integration of microsatellite-based genetic maps for the turkey (Meleagris gallopavo)
Bibliographic record
Abstract
Integration of turkey genetic maps and their associated markers is essential to increase marker density in support of map-based genetic studies. The objectives of this study were to integrate 2 microsatellite-based turkey genetic maps--the Roslin map and the University of Minnesota (UMN) map--by genotyping markers from the Roslin study on the mapping families of the UMN study. A total of 279 markers was tested, and 240 were subsequently screened for polymorphisms in the UMN/Nicholas Turkey Breeding Farms (NTBF) mapping families. Of the 240 markers, 89 were genetically informative and were used for genotyping the F2 offspring. Significant genetic linkages (log of odds > 3.0) were found for 84 markers from the Roslin study. BLASTn comparison of marker sequences with the draft assembly of the chicken genome found 263 significant matches. The combination of genetic and in silico mapping allowed for the alignment of all linkage groups of the Roslin map with those of the UMN map. With the addition of the markers from the Roslin map, 438 markers are now genetically linked in the UMN/NTBF families, and more than 1700 turkey sequences have now been assigned to likely positions in the chicken-genome sequence.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".