# Unit Bias

**Details:**

The standard suggested amount of consumption (e.g., food serving size) is perceived to be appropriate, and a person would consume it all even if it is too much for this particular person.

**Datasets:**

(Datasets labeled with this bias will go here.)

**Structural Analysis:**

(Results of dataset analysis for patterns in structure, placement, tone, context, coreference, correlatives, and so on will go here.)

**Proposed Algorithms:**

(Proposed combinations of factors from the above analyses in algorithmic form for automated detection will go here.)

**Positive Detection:**

(Algorithms for bias-positive detection will go here.)

**False-positive Detection:**

(Algorithms for false-positive detection/filtering of the above bias-positive algorithms will go here.)

**Certainty Mapping:**

(Algorithms for the probability mapping between N mathematical dimensions for positive and false-positive algorithms will go here.)

**Successful Algorithms:**

(Tested successful algorithms will go here, along with their respective accuracy metrics and error data for further refinement.)

*References:*

*References:*