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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.

Li Wang1, Qile Hu1, Lu Wang1, Huangwei Shi1, Changhua Lai2, Shuai Zhang3.   

Abstract

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Year:  2022        PMID: 36210468      PMCID: PMC9548111          DOI: 10.1186/s40104-022-00778-0

Source DB:  PubMed          Journal:  J Anim Sci Biotechnol        ISSN: 1674-9782


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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:
Fig. 2

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

Fig. 3

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; b was the bias; F was the activation function

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; b was the bias; F was the activation function The original publication has been corrected.
  1 in total

1.  Predicting the growth performance of growing-finishing pigs based on net energy and digestible lysine intake using multiple regression and artificial neural networks models.

Authors:  Li Wang; Qile Hu; Lu Wang; Huangwei Shi; Changhua Lai; Shuai Zhang
Journal:  J Anim Sci Biotechnol       Date:  2022-05-13
  1 in total

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