An iterative procedure for deriving selection indexes with constant restrictions1
Bibliographic record
Abstract
The objective of this study was to present an iterative procedure for deriving selection indexes with constant restrictions. Constant restriction means that the genetic responses of the restricted traits are preset to actual amounts for a given selection intensity (ī). Results of this study show that an index with constant restriction alone or in combination with other types of restrictions possesses three distinctive characteristics: 1) the coefficient matrix of the index equations is not symmetric and is nonlinear; 2) the coefficient matrix contains unknown ī, indicating that the index coefficients (b) to be derived depends on the value of ī predetermined before selection; and 3) the coefficient matrix contains unknown b, thus requiring iterative methods to solve the index equations. As a result of these unique characteristics, the index coefficients, genetic responses of the index traits, and overall genetic gain in net merit change nonlinearly with varying levels of ī, which is in sharp contrast to both unrestricted and restricted indexes reported in the literature. The construction of a constant-restricted index requires predetermining the value of ī intended for a selection program to derive the corresponding b. An index with constant restrictions has no meaning unless it is associated with a specific value of ī. Numerical examples are given to illustrate the construction of the index with constant restrictions and to validate the theoretical development proposed. The derived equations have yielded an index that maximized the total merit and fulfilled constant restriction at the same time.
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".