Development of a new composite SLICK beef cattle breed for improved tolerance to heat stress in temperate environments
Bibliographic record
Abstract
Heat stress in cattle ( Bos taurus) is an increasing concern for the North American beef industry due to climate change, even in temperate regions. The SLICK phenotype, a dominant trait from Senepol cattle, enhances heat tolerance, but extreme cold events also pose a challenge. To address both climatic extremes, we developed a composite breed (3/8 Senepol, 1/8 Red Angus, 1/2 Galloway) and evaluated its tolerance to heat strain using respiration rate, activity levels, rumen temperatures, and heat shock protein 70 (HSP70) expression. Seventeen composites (11 SLICK, 6 non-SLICK) and 6 Angus controls were assessed under varying temperature-humidity index (THI) conditions. SLICK composites exhibited significantly lower respiration rates ( p < 0.05) and maintained higher activity levels ( p < 0.05) at THI levels above 72 compared to non-SLICK composites and Angus controls. Rumen temperatures among all groups were negatively correlated with THI. No significant differences in HSP70 expression were observed between groups ( p > 0.05). We demonstrated that our SLICK composites possessed superior phenotypic heat-tolerant traits. These findings confirm that the heat tolerance conferred by the SLICK genotype remains effective despite the introduction of Galloway genetics, supporting the heat strain viability of this composite breed for cattle production in temperate regions.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.002 | 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".