Abstract
The objectives of the 2 studies conducted were to validate the accuracy of an automated monitoring device (AMD; HR-LDn tags, SCR Engineers Ltd., Netanya, Israel) for different types of behaviors or cow-states (side lying, resting, medium activity, high activity, rumination, grazing, walking, and panting) in beef heifers and to determine if the total time per cow-state recorded by the AMD corresponds to the total time per cow-state recorded by instantaneous observations. Cow-state is recorded every second and, within 1 min, the most prevalent cow-state is considered to be the behavior presented by the animal during that interval. Study personnel (n = 2) observed heifers (n = 10) for 20 min from 0800 to 1140 h and 10 min from 1500 to 1640 h during 4 consecutive days and recorded continuously each cow-state at started and ended. Thus, study personnel were able to determine within a 1-min interval, which cow-state was most prevalent and represented the heifer’s behavior. Because the proprietary machine learning algorithm prioritizes certain behaviors over others based on their contribution to the understanding of generalized bovine behavior patterns, we also determined the most prevalent behavior observed in 5-min intervals. Test characteristics (sensitivity, specificity, accuracy, and negative and positive predicted values) were calculated using the observer as the gold standard. In study 2, heifer behavior was scanned by observers (n = 2) every 5 min from 0800 to 1100 h and 1500 to 1800 h for 3 consecutive days. Total minutes per cow-state according to the observer were compared with the total minutes per cow-state according to the AMD during the same period to determine the correlation coefficient. In study 1, test characteristics were high (low ≤ 40%, moderate = 41 to 74%, high ≥ 75%) for rumination (≥ 89.7%), grazing (≥ 76.5%), and side lying (≥ 81.8%), and moderate for resting (≥ 48.8%). In study 2, the correlation coefficient for rumination (R2 = 0.92) and grazing (R2 = 0.90) were high and the correlation coefficient for resting (R2 = 0.66) and walking (R2 = 0.33) were moderate. We conclude that the AMD used in this study showed high accuracy when measuring rumination and grazing, but it was subpar when measuring resting and walking. The algorithms employed by the AMD used need to be improved for determination of walking and resting behaviors of beef cattle.
Topological Models for Prediction of Pharmacokinetic Parameters of Cephalosporins using Random Forest, Decision Tree and Moving Average Analysis
