Marker-assisted estimation of quantitative genetic parameters in rainbow trout, <i>Oncorhynchus mykiss</i>
Bibliographic record
Abstract
Estimation of quantitative genetic parameters conventionally requires known pedigree structure. However, several methods have recently been developed to circumvent this requirement by inferring relationship structure from molecular marker data. Here, two such marker-assisted methodologies were used and compared in an aquaculture population of rainbow trout (Oncorhynchus mykiss). Firstly a regression-based model employing estimates of pairwise relatedness was applied, and secondly a Markov Chain Monte Carlo (MCMC) procedure was employed to reconstruct full-sibships and hence an explicit pedigree. While both methods were effective in detecting significant components of genetic variance and covariance for size and spawning time traits, the regression model resulted in estimates that were quantitatively unreliable, having both significant bias and low precision. This result can be largely attributed to poor performance of the pairwise relatedness estimator. In contrast, genetic parameters estimated from the reconstructed pedigree showed close agreement with ideal values obtained from the true pedigree. Although not significantly biased, parameters based on the reconstructed pedigree were underestimated relative to ideal values. This was due to the complex structure of the true pedigree in which high numbers of half-sibling relationships resulted in inaccurate partitioning of full-sibships, and additional unrecognized relatedness between families.
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How this classification was reachedexpand
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 teacher head, 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".