Genetic parameters of live body weight, body measurements, greasy fleece weight, and reproduction traits in Makuie sheep breed
Bibliographic record
Abstract
Genetic parameters of production and reproduction traits were estimated using 39,926 records from 5,860 individual progenies of 163 sires and 1,558 dams. The data were collected at Makuie Sheep Breeding and Raising Station (Maku, Iran) from 1989 through 2013. Nineteen traits were classified in four main groups: a) live body weight traits, b) body measurement traits, c) greasy fleece weight traits, and d) reproduction traits. Year of birth, lamb sex, age of dam, and birth type were considered as fixed effects in the animal model. Four different animal models that are differentiated by including or excluding maternal effects were fitted for each trait. The Akaike information criterion was used to determine the most appropriate model for each trait. Parameters were overestimated substantially when maternal effects, either genetic or environmental, were ignored from the models. By ignoring the maternal effects, the traits could be classified into three main groups: body live weight traits with high heritability (0.34-0.46), body measurement and greasy fleece weight traits with medium heritability (0.11-0.27) and reproduction traits with low heritability (0.03-0.20). The genetic correlations among the traits ranged from-0.41 to 0.99. The estimated genetic parameters may be used to set up short/long term breeding program for the selection purpose of Makuie sheep breed.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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 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".