Regulation of body temperature in Canadian beef cattle of various immune response phenotypes when the temperature humidity index is above normal
Bibliographic record
Abstract
Abstract Climate change with increases in ambient temperature and humidity, affect animal life dramatically decreasing their production and reproduction potential, as well as making them more susceptible to different diseases. This leads to an increase in their core body temperature and respiration rate which in turn decreases their production, reproduction, and immune function. The High Immune Response (HIR™) technology which has been developed at the University of Guelph ranks animals using two arms of adaptive immune system to high (H), average (A), and low (L) immune responders. (H) immune responders have been reported to have fewer incidents of disease and robust immune response. (H) immune responders have been also reported to have better colostrum and milk quality making them an ideal model to examine the effects of climate change on health traits. The objective of this study was to check if (H) immune responder beef cattle are able to regulate their rectal temperature better. In this study, rectal temperatures of 36 beef cows were recorded during normal Temperature-Humidity Index (THI<74) and above normal THI (THI ≥ 74) once in the morning and once in the afternoon. Results indicated that 62.5% of cows showed increases in rectal temperature in THI ≥ 74. However, mean rectal temperatures of (H) antibody-mediated immune responders did not differ significantly during THI < 74 and THI ≥ 74; indicating that these high antibody responder beef cows were better able to regulate their body temperatures. This is the first time that genetic regulation of an immune response trait has been shown to influence the response to in vivo heat stress indicating that it may be possible to select cattle with both improved health and heat tolerance.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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".