Analyses of lambing dates in sheep breeds using von Mises distribution
Bibliographic record
Abstract
Regular changes in the environment and biological responses generate seasonal patterns in the reproduction in small ruminants. Breeding seasonality is a significant constraint impacting efficiency of lamb production. However, seasonality-related traits present a special peculiarity from a statistical point of view being circular data (day of year running 1:365). Recently, circular mixed models have been developed on the basis of the von Mises distribution and were applied to analyse lambing day distribution recorded from five major Canadian sheep breeds (Rideau Arcott, Romanov, Dorset, Suffolk and Polypay). In a simulation study, the linear model was not able to capture the variance components simulated under the circular paradigm; however, the von Mises model evidenced its ability to infer the variance components of simulated circular records. Using real data of sheep, mostly negligible variances were observed for additive genetic effect when using a linear model on circular data values. In contrast, when using the von Mises model, genetic variances were different across breeds, and it raises the possibility to delay the peak of reproduction and to change the seasonality of the ewes. However, a large variance was captured by flock-year effects emphasizing the strong influence of management in lambing seasons for Canadian sheep populations. Finally, the results suggest the potential of using the von Mises model to analyse circular data, and further research is needed for better understand the complexity of this trait and the von Mises models.
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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 | 0.006 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".