PSI-8 Comparison of heat stress behaviour between different Canadian Bos taurus cattle breeds using unmanned aerial vehicles
Bibliographic record
Abstract
Abstract Heat stress is an emerging cause of mortality and production loss in Bos taurus beef cattle production in North America. Despite the recent occurrence of extreme heat events in Canadian pastures and feedlots, there is very little heat stress research conducted in Canadian settings. The purpose of this study was to develop a non-invasive method to compare behavioral and physiological indices of heat stress between different Canadian cattle breeds. We used thermal imagery acquired by an unmanned aerial vehicle (UAV) to compare surface temperatures between two colour variants of Black Angus x Canadian Speckle Park calves on pasture. The mean back surface temperature for dark variants (n = 5) was 38.6 °C (SD = 4.9), whereas for light variants (n = 7) it was 31.3 °C (SD = 3.4). In the subsequent summer, we compared respiration rates between breeds varying in coat colour while in feedlot pens, including Black Angus, Red Angus, Hereford, Simmental, Charolais, the new Canadian Speckle Park composite breed and their various cross breeds. We recorded 4K video of cattle with a UAV positioned at nadir directly overhead at a height of ~10–15 m; respiratory behavior was analyzed later using Observer XT software. The mean respiration rate in breaths per minute (BPM) for black coated cattle (110 BPM, SD = 19) and red coated cattle (105 BPM, SD = 20) was higher than white coated cattle (94 BPM, SD = 21). We conclude that dark-coated cattle show heightened responses to hot temperatures due to increased absorption of solar radiation at the coat; as a result, dark-coated cattle are likely more susceptible to heat-stress related production losses than light-coated cattle under temperate summer weather conditions. We further conclude that UAVs are a novel and non-invasive tool to study cattle heat stress behavior in feedlot and pasture settings.
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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 | 0.001 |
| 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) | 0.002 | 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".