Generic Person Genderizer
Output
The person is clearly 'male'.
The person is clearly 'female'.
Can be either male or female. See malePercent.
No gender could be computed, but better intelligence should be able to tell the gender. An example is a name input of which we have never heard before.
From the given data it is or seems impossible to tell the gender.
For example all terms are gender-inapplicable, or there are no names at all.
Thus this differs from NEUTRAL where something is clearly known to be neutral.
There are conflicting genders in the given data.
Example: "Mr Daniela Miller" (salutation vs. given name).
The input data must be manually reviewed. It is impossible and useless to make a guess (garbage in would only cause garbage out).
null) then this may be specified (but does not have to be), 0-100, the remaining % are for female.