Technical Note: Software for Calculation of the Inverse Numerator Relationship Matrix
Bibliographic record
Abstract
Software was developed to compute the non-zero elements of the inverse of the numerator relationship matrix used in estimation of (co)variances and breeding values. The program was written to be flexible with regard to format of the input pedigree file and integration with existing software packages for genetic evaluation. The program was further designed to be highly portable, with a minimum of compiler dependence, and to use dynamic rather than static memory allocation. Real time required to read and sort input pedigrees containing 5,000 to 100,000 animals with varying levels of inbreeding and to compute the non-zero elements of the matrix was < 9.5min and increased as the numbers of animals in the pedigree file increased beyond 20,000 animals. Increased numbers of inbred animals in large (≥20,000 animals) pedigrees increased the time required to complete computations. Although a combination of alphanumeric animal identification codes were allowed, the time required to initially read pedigrees and, therefore, total run time was significantly decreased (P < 0.001) for large pedigrees when identification codes were strictly numeric for all animals.
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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 | 0.020 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.093 | 0.073 |
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".