Analyser im Vergleich zu Calf VIGOR Scorer Nutzung und Statistiken

A visual machine learning/statistical analysis tool based on Python, also includes a simple Python source code editor. includes popular packages: Numpy, Spicy, Tensorflow, scikit-learn, pandas, statsmodels, opencv, scikit-image, ...
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The Calf Vigor Score app was developed at the University of Wisconsin School of Veterinary Medicine under the direction of Dr. Sheila McGuirk. It is an iOS adaptation of the Calf VIGOR Scoring System created at the University of Guelph through research efforts of Dr. Christine Murray-Kerr and Dr. Ken Leslie, with considerable support from Boehringer-Ingelheim Animal Health in Canada. Dr. Sandra Godden at the University of Minnesota has been instrumental in extension education about the scoring system, as well as the organization and conduct of the major research effort in 2017. Similar to an Apgar scoring system used to assess newborn human infants, the calf VIGOR scoring system is designed to be applied to calves shortly after birth to assesses their Visual appearance, Initiation of movement, General responsiveness, Oxygenation, heart and respiration Rates. The input categories include meconium staining, appearance of the tongue, time to initiation of selective calf movements such as sitting, standing attempts, suckling, head shaking, tongue withdrawal after a pinch, and eye blinking. Mucous membrane color, heart and respiration rates are additional clinical signs that are assessed and assigned scores ranging from 0 to 3. The higher the score, the more vigorous the calf: Total Score - Vitality/Vigor 26 - 27 = Excellent (green) 23 - 25 = Very Good (blue) 21 - 22 = Good (yellow) 18 - 20 = Marginal (black) Less than 18 = Poor (red)
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Januar 15, 2025