503 Late-Breaking: Using Random Regression Models to Estimate Genetic Parameters for Milk Production Traits under Different Levels of Heat Stress in Canadian Holstein Cattle
Bibliographic record
Abstract
Abstract Genetically selecting animals that are heat tolerant can help to maintain production efficiency of the dairy industry under thermal challenges. During the summer months in Canada, dairy cows could be under thermal stress. For instance, in Ontario, cows are expected to be 105 days under thermal stress. The objective of this study was to estimate genetic parameters for milk production traits under heat stress in Canadian Holstein cows. A total of 604,050 test-day records for milk, fat, and protein yields from 78,516 first-lactation Holstein cows were used in this study. The production data was combined with daily maximum temperature-humidity index (THI; ranging from 30 to 83) calculated based on meteorological records from public weather stations. In total, 58 weather stations were located within a maximum distance of 20 km from each of the 1,284 herds included in this study. Single-trait random regressions on THI, fitting linear splines to describe the random additive genetic and permanent environmental effects, were used in the genetic analyses. Based on preliminary analyses, the knots of the spline functions were set to THI = 70, 57, and 58 for milk, fat, and protein yields, respectively. Therefore, the models assumed a linear effect of heat stress beyond the THI threshold, allowing to estimate the additive genetic effect under thermal comfort and at different levels of heat stress. The genetic correlations estimated between thermal comfort and heat stress were -0.09, -0.33, and -0.21 for milk, fat, and protein yields, respectively. This indicates an antagonistic relationship between level of production and heat tolerance. However, small differences in heritability estimates were observed above the THI thresholds (they ranged from 0.20 to 0.22 for milk, 0.18 to 0.15 for fat, and 0.14 to 0.15 for protein yields). This study demonstrates the possibility of genetically selecting for more heat-tolerant animals in Canada.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".