In-depth pedigree analysis in a large Brazilian Nellore herd
Why this work is in the frame
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Bibliographic record
Abstract
A large herd of Nellore cattle was evaluated using in-depth pedigree analyses. Taking into account the incomplete pedigree due to the use of multiple young sires for mating, the average inbreeding coefficient was calculated as 1.73% for the last generation, which was higher than the regular inbreeding coefficient (0.25%). The effective population size was estimated to be 114, 245, and 101 for the time periods 1995-1999, 1999-2003, and 2003-2007, respectively. Parameters based on the probability of gene origin were used to describe the genetic diversity over time in the herd. The effective number of founders, ancestors, and founder genomes decreased over time, showing an overall loss of genetic diversity. In the last five-year period (2003-2007), based on available pedigree information, one prominent ancestor contributed 10.6% to the gene pool of the herd, and 30% of this pool was contributed by 31 ancestors. The analysis of inbreeding under random mating indicated that the mating strategies used in the herd are slowing down inbreeding rates. However, it is advisable to continue monitoring the inbreeding rates and genetic diversity in this herd in the future.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it