Validation of triaxial accelerometers to measure the lying behaviour of adult domestic horses
Bibliographic record
Abstract
Examining the characteristics of an animal's lying behaviour, such as frequency and duration of lying bouts, has become increasingly relevant for animal welfare research. Triaxial accelerometers have the advantage of being able to continuously monitor an animal's standing and lying behaviour without relying on live observations or video recordings. Multiple models of accelerometers have been validated for use in monitoring dairy cattle; however, no units have been validated for use in equines. This study tested Onset Pendant G data loggers attached to the hind limb of each of two mature Standardbred horses for a period of 5 days. Data loggers were set to record their position every 20 s. Horses were monitored via live observations during the day and by video recordings during the night to compare activity against accelerometer data. All lying events occurred overnight (three to five lying bouts per horse per night). Data collected from the loggers was converted and edited using a macro program to calculate the number of bouts and the length of time each animal spent lying down by hour and by day. A paired t-test showed no significant difference between the video observations and the output from the data loggers (P=0.301). The data loggers did not distinguish standing hipshot from standing square. Predictability, sensitivity, and specificity were all >99%. This study has validated the use of Onset Pendant G data loggers to determine the frequency and duration of standing and lying bouts in adult horses when set to sample and register readings at 20 s intervals.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".