Genome selection for predicting the estimated breeding value of Canadian Holstein cattle
Bibliographic record
Abstract
The objective of this study is to identify a subset of SNPs from a genome-wide dense panel of SNPs to predict the estimated breeding values (EBVs) of bulls. This requires selecting variables from a high-dimensional variable space (thousands of SNPs) with a much smaller sample size (hundreds of available animals). In this thesis, a two-step variable selection strategy is proposed to tackle this problem. In the first step, 10,000 randomly selected models were analyzed using the least angle regression (LARS) method. A subset of SNPs that were found to have significant effects on the EBVs among the 10,000 randomly selected models would be considered as the input SNPs for the second step. In the second step, the least absolute shrinkage and selection operation (LASSO) variable selection method was used to obtain a final model for predicting the EBVs. In addition, the sparse partial least squares (SPLS) method was considered in the place of the LASSO. The performances of the LASSO and SPLS were compared.
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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".