Effect of core body temperature, time of day, and climate conditions on behavioral patterns of lactating dairy cows experiencing mild to moderate heat stress
Bibliographic record
Abstract
Cattle show several responses to heat load, including spending more time standing. Little is known about what benefit this may provide for the animals. Data from 3 separate cooling management trials were analyzed to investigate the relationship between behavioral patterns in lactating dairy cows experiencing mild to moderate heat stress and their body temperature. Cows (n=157) were each fitted with a leg data logger that measured position and an intravaginal data logger that measures core body temperature (CBT). Ambient conditions were also collected. All data were standardized to 5-min intervals, and information was divided into several categories: when standing and lying bouts were initiated and the continuance of each bout (7,963 lying and 6,276 standing bouts). In one location, cows were continuously subjected to heat-stress levels according to temperature-humidity index (THI) range (THI≥72). The THI range for the other 2 locations was below and above a heat-stress threshold of 72 THI. Overall and regardless of period of day, cows stood up at greater CBT compared with continuing to stand or switching to a lying position. In contrast, cows lay down at lower CBT compared with continuing to lie or switching to a standing position, and lying bouts lasted longer when cows had lower CBT. Standing bouts also lasted longer when cattle had greater CBT, and they were less likely to lie down (less than 50% of lying bouts initiated) when their body temperature was over 38.8°C. Also, cow standing behavior was affected once THI reached 68. Increasing CBT decreased lying duration and increased standing duration. A CBT of 38.93°C marked a 50% likelihood a cow would be standing. This is the first physiological evidence that standing may help cool cows and provides insight into a communally observed behavioral response to heat.
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 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 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".