Genetic variability of growth curves in dairy heifers
Bibliographic record
Abstract
The objective of this study was to evaluate the variability of growth curves of dairy heifers and estimate genetic parameters. 15066 records taken from birth until 26 months (808 days) on 2754 heifers of Quebec were considered. The pedigree file comprised 10123 animals. The Mixed procedure of SAS with ordinary polynomials was used for simple phenotypic analyses, fitting fixed linear, quadratic and cubic regressions of body weight (in kilograms) on age (in months) as well as random intercept, and random linear and quadratic regressions for each animal. The Wombat program (Meyer, 2007), with Legendre polynomials was used to estimate the genetic parameters by fitting fixed herd-year-season of birth and quartic regression of body weight on age in days, as well as random regressions for quadratic additive genetic and cubic permanent environmental effects. Heritability estimates of body weight ranged from 0.22 at around 70 days to 0.45 at around 210 days. Heritabilities of body weight at birth and 808 days were 0.35 and 0.32, respectively. The additive genetic correlations between body weights at different ages ranged from -0.37 to 1.00. In general, the genetic correlations were higher than the permanent environmental and phenotypic correlations.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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".