Heritabilities for the puppy weight at birth in Labrador retrievers
Bibliographic record
Abstract
BACKGROUND: Weight at birth is an important predictor of neonatal mortality and morbidity in dogs. In addition, the birthweight of the puppies in a litter influences the decision to perform a cesarean section. The goal of the present study was to estimate heritabilities for the puppy birth weight in Labrador retrievers. RESULTS: Of the 1138 Labrador retriever litters whelped at the Guiding Eye for the Blind between September 2001 and February 2018, 1013 were included in the analyses after data editing. Puppy weight at birth was the target trait, measured on a continuous scale in pounds, and converted to grams. Linear mixed models were used to identify factors influencing puppy weight at birth. The analyses showed that the sex of the puppy, litter size, length of gestation, adult weight of the dam, parity, year of birth and inbreeding coefficient of the puppies and dams contributed to the variance of the puppy birth weight. Dam and litter effects were included as random effects. A multiple trait derivative free restricted maximum likelihood approach was used to estimate variance components and genetic parameters with two animal models, one without covariates (Model 1) and one with covariates (Model 2). Sex of the puppy and litter size had moderate effects, whereas gestation length, adult weight of the dam, parity, year of birth and inbreeding coefficients of the dam and the puppies had minor effects. Estimates for Model 1 and Model 2 were 0.21 and 0.17 for the direct heritabilities, 0.22 and 0.22 for the maternal additive genetic heritabilities, 0.07 and 0.07 for the maternal permanent environmental proportions, and 0.14 and 0.08 for the environmental proportion of the litter. CONCLUSIONS: In order to estimate reliable breeding values for puppy weight at birth, sex of puppy, litter size, length of gestation and the adult weight of the dam should be included. Estimates could benefit from weighing the dams prior to each mating.
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How this classification was reachedexpand
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 | 0.005 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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; both teacher heads agree on what is shown here.
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".