2 resultados para regression discrete models

em eResearch Archive - Queensland Department of Agriculture


Relevância:

80.00% 80.00%

Publicador:

Resumo:

Objective To identify measures that most closely relate to hydration in healthy Brahman-cross neonatal calves that experience milk deprivation. Methods In a dry tropical environment, eight neonatal Brahman-cross calves were prevented from suckling for 2–3 days during which measurements were performed twice daily. Results Mean body water, as estimated by the mean urea space, was 74 ± 3% of body weight at full hydration. The mean decrease in hydration was 7.3 ± 1.1% per day. The rate of decrease was more than three-fold higher during the day than at night. At an ambient temperature of 39°C, the decrease in hydration averaged 1.1% hourly. Measures that were most useful in predicting the degree of hydration in both simple and multiple-regression prediction models were body weight, hindleg length, girth, ambient and oral temperatures, eyelid tenting, alertness score and plasma sodium. These parameters are different to those recommended for assessing calves with diarrhoea. Single-measure predictions had a standard error of at least 5%, which reduced to 3–4% if multiple measures were used. Conclusion We conclude that simple assessment of non-suckling Brahman-cross neonatal calves can estimate the severity of dehydration, but the estimates are imprecise. Dehydration in healthy neonatal calves that do not have access to milk can exceed 20% (>15% weight loss) in 1–3 days under tropical conditions and at this point some are unable to recover without clinical intervention.

Relevância:

30.00% 30.00%

Publicador:

Resumo:

Partial least squares regression models on NIR spectra are often optimised (for wavelength range, mathematical pretreatment and outlier elimination) in terms of calibration terms of validation performance with reference to totally independent populations.