Validation of an accelerometer to quantify inactivity in laying hens with or without keel-bone fractures
Bibliographic record
Abstract
Accelerometers are used to remotely monitor activity in various species in studies that quantify pain, document behavioural patterns, and measure individual activity differences. Studies validating accelerometers typically quantify various active states; however, targeting states specific to periods of inactivity, such as sitting, sleeping, and standing, has the potential to more accurately quantify inactive behaviours commonly associated with behavioural changes related to pain, sickness, or injury. Our objectives were two-fold: first, validate a commercially available accelerometer (Actical®) for quantifying inactivity in laying hens and, second, compare inactivity levels between hens with severely fractured keel bones and hens with minimal to no keel damage. Correlation between the inactivity level as measured by the accelerometer compared to live, focal observation of stationary, inactive behaviours was high; therefore, the Actical® accurately quantifies inactive states in laying hens. Following validation, the Actical® accelerometer was used to quantify inactivity level differences between hens with or without keel-bone damage. Severely fractured hens spent less time motionless, than hens with minimal to no keel damage. Further investigation into inactivity differences related to keel status before and after acquisition of keel fractures is warranted. Use of the accelerometer has the potential to improve animal welfare research by quantifying the effect of pain or sickness on activity level, mapping daily activity patterns, and measuring individual differences in general activity.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 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".