Estimation of Genetic Parameters of Heat Tolerance for Production Traits in Canadian Holsteins Cattle
Bibliographic record
Abstract
Understanding how cows respond to heat stress has helped to provide effective herd management practices to tackle this environmental challenge. The possibility of selecting animals that are genetically more heat tolerant may provide additional means to maintain or even improve the productivity of the Canadian dairy industry, which is facing a shifting environment due to climate changes. The objective of this study was to estimate the genetic parameters for heat tolerance of milk, fat, and protein yields in Canadian Holstein cows. A total of 1.3 million test-day records from 195,448 first-parity cows were available. A repeatability test-day model fitting a reaction norm on the temperature-humidity index (THI) was used to estimate the genetic parameters. The estimated genetic correlations between additive genetic effect for production and for heat tolerance ranged from -0.13 to -0.21, indicating an antagonistic relationship between the level of production and heat tolerance. Heritability increased marginally as THI increased above its threshold for milk yield (0.20 to 0.23) and protein yield (0.14 to 0.16) and remained constant for fat yield (0.17). A Spearman rank correlation between the estimated breeding values under thermal comfort and under heat stress showed a potential genotype by environmental interaction. The existence of a genetic variability for heat tolerance allows for the selection of more heat tolerant cows.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".