Phenotypic and Genetic Analyses of Heat Tolerance in Holsteins using NASA Prediction of Worldwide Energy Resources (POWER) Weather Data
Bibliographic record
Abstract
Heat stress can negatively impact the sustainability and productivity of a dairy production system even in temperate regions such as Canada. A possible mitigation strategy is to use genetic selection to improve heat tolerance. However, it is difficult to study heat stress using weather station data due to the sparsity of stations and inconsistencies within datasets. Therefore, the aim of this study was to assess the impact of heat stress and estimate genetic parameters for heat tolerance in Canadian Holsteins using an alternative meteorological data resource known as NASA POWER. The results showed that NASA POWER estimates were closely correlated to weather station values and revealed that heat stress negatively affects milk, protein, and fat yield in Canadian dairy cattle, there is individual variation in heat tolerance which has a low to moderate heritability, and a genotype-by-environment interaction may strongly affect milk and protein yield causing the re-ranking of top-ranked bulls in different thermal environments.
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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 | 0.001 |
| 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".