How do I narrow down my Reverse Look Up results? What’s the theory behind Reverse Look Up?

Just want to take a minute to point out what you are essentially doing with the reverse lookup.
When you put in your frequency, the program will look at any database set that has a frequency that is within your % tolerance.
First question you have to ask is, is the % tolerance used even valid. 0.25% is too big. 0.06% is too big. Using anything over 0.025% is too big, and in some cases even 0.025% is too big.
When you are not using 0.0% tolerance, which would be an exact match to a frequency in the database, you then rely on resonance overlap.
This is akin to saying I have an apple, and I'll take any database set that has fruit in it.
Matching with too large a % is like saying I have an apple, and I'll take anything round -- even a baseball.
Almost all results returned are based on % tolerance matching. Very few are ever an exact frequency match. Even fewer are ever actual true resonance overlap matches. It helps to do the math by hand afterwards and really look at what made the connection.
Second, you have the option to use octave matches. This will look for any database set that has a frequency that is not only within the +- range you enabled with % tolerance, but any frequency that is double or halved (even multiple times). This is why the list gets so much larger when you turn this feature on.
So, you can essentially get the entire database reported back if you want by using the right (but inappropriately selected) values.
People love labels. Reverse lookup would have some meaningful use if it was an accurate and validated map of frequencies to pathogens, and even better if it was fairly complete list at that.
However, when you get back nothing, people usually are then disappointed. So, they increase the values until they get validation when a frequency program finally shows up.
All it does is provide statistical matchups to a data set and allows people to confirm their own biases.
The only valid use for reverse lookup is to compare biofeedback results against prior biofeedback results to spot patterns.
It could also be very useful to compare biofeedback results against others with similar conditions. To try and spot common frequencies that are shared amongst a large group across a large sample set has value. Perhaps this would allow us to actually find new frequencies.
Instead, it is used all too often to throw away biofeedback hits in order to run frequency programs that have the labels that one's own biases seek.

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