A simultaneous procedure for deriving selection indexes with multiple restrictions1
Bibliographic record
Abstract
The formulas given in literature for the construction of restricted indexes were designed only for the imposition of a single restriction (zero, fixed, or proportional). This study presents both the theory and the methods of a simultaneous procedure for constructing indexes with single or multiple restriction(s). Numerical examples are given to verify the theoretical development and to demonstrate the implementation of the procedure. The simultaneous procedure presented brings the construction of various restricted indexes into a simple computational scheme. In addition to the use of the proposed procedure to handle multiple traits, it can be used to modify the growth curve of meat animals or the lactation curve of dairy animals, which generally requires simultaneous imposition of different restrictions on different stages of the curves. A misconception in the literature is that the variance of an index (b'Pb) is not equal to the covariance between an index and its net merit (b'Ga) when the index is a restricted one. This study showed generally that b'Pb and b'Ga are equal in the restricted or unrestricted case only when elements of b represent the original solutions from the index equations and are not equal when elements of b are expressed in terms of proportions.
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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 | 0.005 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".