Systems and methods for classification of scholastic works

1. A system for classification of scholastic works, the system comprising a computing device,

wherein the computing device is configured to:

receive a first scholastic work including a plurality of textual elements;

identify an author and a category of the first scholastic work;

determine at least a work theme, wherein determining further comprises:

receiving theme training data, the theme training data including a plurality of entries, each entry including a training textual element and a correlated theme;

training a theme classifier as a function of the training data; and

determining the at least a work theme as a function of the plurality of textual elements and the theme classifier;

calculate a reliability quantifier as a function of the at least a work theme, the author, and the category;

select the scholastic work as a function of the reliability quantifier;

derive, from the scholastic work, at least a correlation between a dietary practice and alleviation of a disease state; and

store the at least a correlation in an expert database.

2. The system of claim 1 , wherein calculating the reliability quantifier comprises determining an expert prestige factor, wherein the expert prestige factor quantifies the prestige of the author.

3. The system of claim 2 , wherein the expert prestige factor relates to the reliability of the author.

4. The system of claim 2 , wherein the expert prestige factor is based on the reputation of the author in the scientific community of the author.

5. The system of claim 2 , wherein the computing device is further configured to:

recalculate the reliability quantifier, wherein recalculating the reliability quantifier comprises recalculating the expert prestige factor; and

remove the at least a correlation from the expert database as a function of the recalculated reliability quantifier.

6. The system of claim 1 , wherein calculating the reliability quantifier further comprises:

receiving a plurality of publications by the at least an author;

training an author theme classifier using the theme training data;

identifying at least an author theme as a function of the plurality of publications and the author theme classifier;

comparing the at least an author theme to the theme; and

calculating the reliability quantifier as a function of the comparing.

7. The system of claim 1 , wherein calculating the reliability quantifier further comprises:

identifying a publisher of the first scholastic work;

determining at least a publisher theme of the publisher;

comparing the at least a publisher theme to the theme; and

calculating the reliability quantifier as a function of the comparing.

8. The system of claim 6 , wherein determining the at least a publisher theme further comprises:

receiving a plurality of publications of the publisher;

training a publisher theme classifier using the theme training data; and

identifying the at least a publisher theme as a function of the plurality of publications and the publisher theme classifier.

9. The system of claim 1 , wherein the computing device is further configured to:

receive unfavorable scholarship of the first scholastic work; and

remove the at least a correlation from the expert database.

10. The system of claim 9 , wherein receiving unfavorable scholarship of the first scholastic work comprises:

receiving a second scholastic work;

identifying at least a citation of the first scholastic work in the second scholastic work; and

recalculating the reliability quantifier as a function of the at least a citation.

11. A method of classification of scholastic works, the method comprising:

receiving, at a computing device, a first scholastic work including a plurality of textual elements;

identifying, by the computing device, an author and a category of the first scholastic work;

determining, by the computing device, at least a work theme, wherein determining further comprises:

receiving theme training data, the theme training data including a plurality of entries, each entry including a training textual element and a correlated theme;

training a theme classifier as a function of the training data; and

determining the at least a work theme as a function of the plurality of textual elements and the theme classifier;

calculating, by the computing device, a reliability quantifier as a function of the at least a work theme, the author, and the category;

selecting, by the computing device, the scholastic work as a function of the reliability quantifier;

deriving, by the computing device and from the scholastic work, at least a correlation between a dietary practice and alleviation of a disease state; and

storing, by the computing device the at least a correlation in an expert database.

12. The method of claim 1 , wherein calculating the reliability quantifier comprises determining an expert prestige factor, wherein the expert prestige factor quantifies the prestige of the author.

13. The method of claim 12 , wherein the expert prestige factor relates to the reliability of the author.

14. The method of claim 12 , wherein the expert prestige factor is based on the reputation of the author in the scientific community of the author.

15. The method of claim 12 , further comprising:

recalculating the reliability quantifier, wherein recalculating the reliability quantifier comprises recalculating the expert prestige factor; and

removing the at least a correlation from the expert database as a function of the recalculated reliability quantifier.

16. The method of claim 11 , wherein calculating the reliability quantifier further comprises:

receiving a plurality of publications by the at least an author;

training an author theme classifier using the theme training data;

identifying at least an author theme as a function of the plurality of publications and the author theme classifier;

comparing the at least an author theme to the theme; and

calculating the reliability quantifier as a function of the comparing.

17. The method of claim 11 , wherein calculating the reliability quantifier further comprises:

identifying a publisher of the first scholastic work;

determining at least a publisher theme of the publisher;

comparing the at least a publisher theme to the theme; and

calculating the reliability quantifier as a function of the comparing.

18. The method of claim 17 , wherein determining the at least a publisher theme further comprises:

receiving a plurality of publications of the publisher;

training a publisher theme classifier using the theme training data; and

identifying the at least a publisher theme as a function of the plurality of publications and the publisher theme classifier.

19. The method of claim 11 , further comprising:

receiving unfavorable scholarship of the first scholastic work; and

removing the at least a correlation from the expert database.

20. The method of claim 19 , wherein receiving unfavorable scholarship of the first scholastic work comprises:

receiving a second scholastic work;

identifying at least a citation of the first scholastic work in the second scholastic work; and

recalculating the reliability quantifier as a function of the at least a citation.