Validation of the Ability of a 3D Pedometer to Accurately Determine the Number of Steps Taken by Dairy Cows When Housed in Tie-Stalls
Why this work is in the frame
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Bibliographic record
Abstract
The automation of farm tasks in dairy production has been on the rise, with an increasing focus on technologies that measure aspects of animal welfare; however, such technologies are not often validated for use in tie-stall farms. The objectives of the current study were to (1) determine the ability of the IceTag 3D pedometer to accurately measure step data for cows in tie-stalls, and (2) determine whether the leg on which the pedometer is mounted impacts step data. Twenty randomly selected Holstein dairy cows were equipped with pedometers on each rear leg and recorded for 6 h over three 2-h periods. Two observers were trained to measure step activity and the total number of steps per minute were measured. Hourly averages for right and left leg data were analyzed separately using a multivariate mixed model to determine the correlation between pedometer and video step data as well as the correlation between left and right leg step data. The analysis of the video versus pedometer data yielded a high overall correlation for both the left (r = 0.93) and right (r = 0.95) legs. Additionally, there was good correlation between the left and right leg step data (r = 0.80). These results indicate that the IceTag 3D pedometers were accurate for calculating step activity in tie-stall housed dairy cows and can be mounted on either leg of a cow. This study confirms that these pedometers could be a useful automated tool in both a research and commercial setting to better address welfare issues in dairy cows housed in tie-stalls.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 it