Eye Blink Rates and Eyelid Twitches as a Non-Invasive Measure of Stress in the Domestic Horse
Bibliographic record
Abstract
Physiological changes provide indices of stress responses, however, behavioural measures may be easier to determine. Spontaneous eye blink rate has potential as a non-invasive indicator of stress. Eyelid movements, along with heart rate (HR) and behaviour, from 33 horses were evaluated over four treatments: (1) control—horse in its normal paddock environment; (2) feed restriction—feed was withheld at regular feeding time; (3) separation—horse was removed from visual contact with their paddock mates; and (4) startle test—a ball was suddenly thrown on the ground in front of the horse. HR data was collected every five s throughout each three min test. Eyelid movements and behaviours were retrospectively determined from video recordings. A generalized linear mixed model (GLIMMIX) procedure with Sidak’s multiple comparisons of least squares means demonstrated that both full blinks (16 ± 12b vs. 15 ± 15b vs. 13 ± 11b vs. 26 ± 20a full blinks/3 min ± SEM; a,b differ p < 0.006) and half blinks (34 ± 15ab vs. 27 ± 14bc vs. 25 ± 13c vs. 42 ± 22a half blinks/3 min ± SEM; a,b,c differ p < 0.0001) decreased during feed restriction, separation and the startle test compared to the control, respectively. Eyelid twitches occurred more frequently in feed restriction (p < 0.0001) along with an increased HR (p < 0.0001). This study demonstrates that spontaneous blink rate decreases while eyelid twitches increase when the horse experiences a stressful situation.
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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 | 0.001 |
| 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) | 0.001 | 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".