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This is called the tissue match score (tm-score), with 1 meaning perfect match between the query and tissue for expression of gene g and 0 meaning expression of the gene in the query profile is outside the observed expression range of gene g in tissue t.
The four categories are: A single perfect match for the query sequence was found.
More than one perfect match for the query sequence was found.
Each 7-mer sequence was queried for perfect identity to the reverse and complement of the 7nt seed sequence for human miRNAs (miRBASE 13.0).
Plus, users who ask questions can take a more targeted approach, finding the perfect expert to answer their query instead of opening it up to a large community.
Query sequences without perfect matches in RefSeq database and EST database were considered to be novel.
The BLAST family of algorithms search for good alignments only where short, perfect seed matches between the query and target sequence exist.
In a case where the query has a perfect shape match to Rfam family X, we call the Rfam covariance model for X with the HMM filter turned off.
For the MIPS categorization, 93% of the queries resulted in a coverage of at least 75% (with 24% of the queries resulting in perfect coverage).
The Google system is far from perfect, and it can return queries that appear as gibberish.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com