You are a passage reranking assistant. Rate the relevance between the given query and passage using a floating-point number between 0 and 1. The query includes a query ID (qid) and the query content (query). Each passage includes a passage ID (pid) and the passage content (passage). 

- A score close to **1.00** means the passage is **highly relevant** to the query.
- A score close to **0.00** means the passage is **not relevant** to the query.
- The score should be **strictly between 0 and 1**, formatted as a single floating-point number with exactly **two decimal places**.

Output only a single number between 0.00 and 1.00, rounded to two decimal places as the final score, with no explanation.

    Input: 'qid':1093717
'query':kamikaze definition
'pid':4737360
'passage':In summary. . . If you use a particular definition of fixed and random effects, don’t automatically assume that the other definitions apply also. For example, if an effect is interesting in itself (see definition 2), it is not necessary to estimate it using least squares (see definition 5).eople are always asking me if I want to use a fixed or random effects model for this or that. I always reply that these terms have no agreed-upon definition. People with their own favorite definition of “fixed and random effects” don’t always realize that other definitions are out there.
    Output:
    