Scalar methods to deregress and split genomic predictions, and associated behavior of simple regressions, for later use in combined prediction and validations
Bibliographic record
Abstract
Scalar deregressions are used in dairy cattle to postprocess genetic evaluations into early and late, or separate, pieces of information. These separate pieces of information are in turn used, for example, to include foreign information or for validation of the genomic evaluation procedure. Here we detail the scalar algebra to separate "partial" evaluations from "whole," with associated breeding values and reliabilities, into equivalent deregressed proofs (pseudophenotypes) and equivalent record contributions (pseudonumber of observed phenotypes). We start from basic principles, and we show several derivations leading to same expressions. The final expressions are similar, but not equal, to other expressions found in the literature. We moreover investigate its use in genetic evaluations and in validations. In evaluation, the new expressions guarantee scalar reversibility (we obtain "whole" evaluations back from deregressed proofs and equivalent record contributions) but not necessarily reversibility of the whole system of equations, in which case matrix-based evaluations would be preferred. In validation, we derive weights (which are very similar but not identical to commonly used weights). We also observe that by construction, the expected value of the regression of the deregressed proof on early predictions is 1, given that early proof is subtracted from late proof, no matter the scale. These derivations help to understand why existing methods work and they may serve as principles to conceive more accurate deregression procedures. We illustrate use of the math with population (large-scale evaluation) examples and with scalar (single-bull) examples.
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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 | 0.012 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.013 | 0.004 |
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".