This indicates that even after merging, additional post-processing may be necessary to ensure the integrity and connectivity of the chunks within the linearization.

Taking an example from the provided link, we can observe how two linearizations, L1 and L2, which have been chunked differently ([B,AEC,D] and [B,ADC,E], respectively), are merged using a prefix-intersection strategy. The resultant merge, [B,A,C,D,E], displays a chunking pattern of [BAC,D,E]. However, to optimize the structure further, post-processing is applied to the merged result, reordering the elements to achieve a new linearization: [A,C,B,D,E], with a refined chunking of [AC,B,D,E].

This post-processing step is crucial as it enhances the ordering and grouping of the chunks, facilitating better data structure representation and potentially improving the performance of algorithms that rely on these linearizations. Therefore, the discussion highlights the significance of not only merging linearizations carefully but also the importance of refining the merged output through thoughtful post-processing techniques.