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Correction: Predicting the growth performance of growing-finishing pigs based on net energy and digestible lysine intake using multiple regression and artificial neural networks models
Journal of Animal Science and Biotechnology volume 13, Article number: 128 (2022)
Correction: J Anim Sci Biotechnol 13, 57 (2022)
https://doi.org/10.1186/s40104-022-00707-1
After publication of this article [1], it was brought to our attention that Figs. 2 and 3 were misplaced, the correct Figs. 2 and 3 are shown below:
The response of ADG on different SID Lys intake (a) and NE intake (b). The curves were generated by the best fitted MR models in training. Only SID Lys intake and SID Lys intake2 were considered as input variables in Fig. 2a while other variables were neglected. Only NE intake and NE intake2 were considered as input variables in Fig. 2b
The structure of the best-fitted artificial neural networks in predicting ADG (a) and F/G (b). H1 was the value in the 1st node in the hidden layer; I1 was the 1st input; am was the bias; O1 was the value of the 1st output variable; H1 was the value of the 1st node; bn was the bias; Factivation was the activation function
The original publication has been corrected.
Reference
Wang L, et al. Predicting the growth performance of growing-finishing pigs based on net energy and digestible lysine intake using multiple regression and artificial neural networks models. J Anim Sci Biotechnol. 2022;13:57. https://doi.org/10.1186/s40104-022-00707-1.
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Wang, L., Hu, Q., Wang, L. et al. Correction: Predicting the growth performance of growing-finishing pigs based on net energy and digestible lysine intake using multiple regression and artificial neural networks models. J Animal Sci Biotechnol 13, 128 (2022). https://doi.org/10.1186/s40104-022-00778-0
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DOI: https://doi.org/10.1186/s40104-022-00778-0