Modeling heat tolerance for production traits in Canadian Holstein cattle.
Bibliographic record
Abstract
Many studies have assessed the effect of heat stress on dairy cattle by integrating an environmental descriptor with test-day records. The genetic parameter of heat tolerance can be estimated using reaction norm models fitting a linear heat stress function (HSf). The HSf commonly fits the temperature-humidity index (THI) as a covariable and assumes a common THI threshold value for the onset of heat stress for all animals. Thus, it does not fully account for individual variation in the THI threshold in the response to heat stress and relies on the determination of a common threshold. The objective of this study was to estimate the genetic parameters and breeding values for heat tolerance by a model fitting Legendre polynomials (LP), which allows for individual variation of THI threshold value in response to heat stress, and to compare the estimated breeding values and genetic parameters to the estimates from a model fitting an HSf with a common THI threshold. Meteorological data collected from the closest weather station to each farm was combined with test-day records from 300,791 first-parity Holstein cows in 4,470 herds across Canada. The LP models from linear to cubic were compared by the mean squared error (MSE) and by inspecting the parameter estimates over the THI gradient. The ranking of bulls' EBV for heat tolerance was compared using the Spearman rank correlation between the LP and HSf models. The quadratic LP model was chosen for the comparison with HSf estimates. The EBV rank correlation for milk production traits under heat stress from the 2 alternate models was greater than 0.97 for all bulls. When the top 10% were compared, the rank correlation between the EBVs from both models was 0.97, 0.95, and 0.86 for fat, protein, and milk yields under heat stress. The results indicate no substantial change in the ranking of bull's EBV. Therefore, both models can identify bulls with high genetic merit for heat tolerance and can be used in genetic evaluations in Canada.
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 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".