Analiza si las conclusiones de la siguiente tabla son correctas. Esta sería la tabla: "\begin{table}[H] 
\begin{center}
%\caption{The table ACCURACY EXPERIMENT 3...............}
\caption{The table elucidates the findings of the experiment conducted within the Red AI. A total of thirty trials were executed, each comprising three iterations of the PBT algorithms, each with distinct hyperparameters. The optimized hyperparameters included maximun features ranging from 10000 to 30000; epochs ranging from 2 to 10 iterations; optimizers such as Adam (AD), RMSprop (Rprop), and SGD; and batch sizes of 32, 64, and 128.}

\label{table:RedIA_Experiment3}
\begin{tabularx}{\textwidth}{crrrrrrrr}
\toprule
\multicolumn{1}{c}{Validation} & \multicolumn{1}{c}{$E_{Total}$ (w)} & \multicolumn{1}{c}{time (s)} & \multicolumn{1}{c}{max} & \multicolumn{1}{c}{max} & \multicolumn{1}{c}{epoch} & \multicolumn{1}{c}{optimizer} & \multicolumn{1}{c}{bach} & \multicolumn{1}{c}{CO\textsubscript{2}e} \\
\multicolumn{1}{c}{Accuracy} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{features} & \multicolumn{1}{c}{len} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{(g/Kwh)} \\
\midrule
49.71&37433.92&1104.51&30000&200&5&SGD&128&7.112 \\
50.14&11500.53&308.01&20000&300&2&SGD&64&2.185 \\
50.37&784.58&1190.19&30000&200&5&SGD&128&0.149 \\
50.44&1465.32&876.20&10000&300&2&SGD&128&0.278 \\
50.60&12907.44&362.60&10000&300&2&SGD&32&2.452 \\
50.71&177.67&331.36&30000&300&2&SGD&64&0.034 \\
51.14&43252.82&1531.88&10000&200&5&SGD&128&8.218 \\
51.59&78.84&108.74&20000&100&2&SGD&64&0.015 \\
51.89&526.51&529.39&30000&200&5&SGD&64&0.100 \\
53.49&36301.74&1290.10&10000&200&10&SGD&64&6.897 \\
54.80&20993.73&692.94&10000&100&10&SGD&32&3.989 \\
54.90&1447.49&634.36&30000&100&10&SGD&32&0.275 \\
82.78&69.70&160.30&30000&100&2&Rprop&32&0.013 \\
83.39&36.56&120.64&30000&100&2&Rprop&64&0.007 \\
83.98&59.84&223.55&20000&200&2&AD&64&0.011 \\
84.53&3622.88&122.31&30000&100&2&Rprop&128&0.688 \\
84.98&168.72&221.93&30000&100&5&AD&64&0.032 \\
85.27&58.44&118.89&20000&100&2&AD&64&0.011 \\
85.28&244.22&458.85&20000&100&5&Rprop&128&0.046 \\
85.29&12772.85&446.51&10000&100&5&Rprop&128&2.427 \\
85.32&203.98&575.88&10000&100&10&AD&32&0.039 \\
85.51&20094.64&739.76&20000&100&10&Rprop&64&3.818 \\
85.53&17832.91&617.82&30000&200&2&Rprop&128&3.388 \\
86.46&58798.03&1791.48&10000&300&5&AD&128&11.172 \\
86.95&9827.10&3291.42&20000&200&10&AD&128&1.867 \\
87.46&709.49&804.46&30000&200&5&Rprop&64&0.135 \\
87.80&87803.41&3275.55&30000&200&10&Rprop&128&16.683 \\
88.12&21998.02&603.18&10000&200&5&Rprop&32&4.180 \\
88.36&7146.22&1923.02&10000&300&10&Rprop&64&1.358 \\
88.44&65218.35&1934.61&10000&300&10&AD&32&12.391 \\
\bottomrule
\end{tabularx}
\end{center}
\end{table}". Las conclusiones que se deben de evaluar son las siguientes: "
The validation accuracy and CO\textsubscript{2} emissions are positively correlated (Table \ref{table:RedIA_Experiment3}). This implies that as validation accuracy increases, so does the CO\textsubscript{2} emissions (measured in grams per kilowatt-hour, g/kWh). Models with higher validation accuracy are more energy-intensive and generate a greater CO\textsubscript{2} emissions. The relationship between validation accuracy and the type of optimizer used (such as sgd, rmsprop, or adam) is not straightforward. However, it is observed that some optimizers, like adam, tend to achieve higher validation accuracy compared to sgd and rmsprop. This difference could be attributed to variations in learning speed and convergence capabilities among optimizers. Additionally, validation accuracy is influenced by factors such as batch size, the number of epochs, and the maximum feature length. In general, increasing these parameters tends to enhance validation accuracy, suggesting that a model trained with more data and additional epochs can achieve greater precision."