A microsatellite linkage map of rainbow trout and its application in QTL analysis
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The majority of species and strains reared globally for aquaculture are relatively unimproved for commercially important traits. The potential for genetic improvement in fish species compared with domestic livestock, is very high. Therefore, we are integrating molecular genetic technologies into aquaculture to help solve some of the major genetic problems. Our long-term goal is to use genetic markers to increase the efficiency of artificial selection in fish stock improvement. To do this, marker-assisted selection (MAS) has been proposed. MAS can be carried out with an understanding of the linkage relationships between quantitative trait loci (QTL) and markers. To identify QTL controlling traits of economic importance, a genetic linkage map is required, with variable markers distributed throughout the genome. We have constructed a genetic linkage map for rainbow trout using 192 microsatellite, 3 RAPD, 5 ESMP, and 7 allozyme markers in three backcross families. As a first step towards MAS, some QTLs associated with economically important traits have been identified using this linkage map. The genetic linkage map based on microsatellites could be useful for QTL analysis in aquaculture.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 it