MIM: an indirect method to assess inbreeding and coancestry in large incomplete pedigrees of selected dairy cattle
Bibliographic record
Abstract
In real data, inbreeding is usually underestimated because of missing pedigree information. A method adapted to the dairy cattle situation is presented to approximate inbreeding when the stored population pedigree is incomplete. Missing parents in incomplete pedigrees were given a dummy identification and assigned to groups (up to nine for a given birth date of progeny). These groups were linked to contemporary reference groups with known parents. An explicit model considered that polygenic breeding values in a censored group were centred on a function of the average breeding value in the corresponding reference group and deviated independently. Inbreeding coefficients were obtained progressively over birth dates starting from founders. For each date considered, the parameters pertaining to its groups were computed using the parameters already obtained from groups belonging to the previous dates. The updating algorithms were given in detail. An indirect method was implemented to expedite mass computations of the relationship coefficients involved (MIM). MIM was compared to Van Raden's (VR) method using simulated populations with 20 overlapping generations and different rates of missing sires and dams. In the situation of random matings, the average inbreeding coefficients by date obtained by MIM were close to true values, whereas they were strongly underestimated by VR. In the situation of assortative matings, MIM gave average inbreeding coefficients moderately underestimated, whereas those of VR's method were still strongly underestimated. The main conclusion of this study adapted to the situation of dairy cattle with incomplete pedigrees was that corrections for inbreeding and coancestry coefficients are more efficient with an explicit appropriate genetic model than without.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".