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':1094316
'query':inverse of natural log function
'pid':2312804
'passage':In many ways, logistic regression is very similar to linear regression. One big difference, though, is the logit link function. The Logit Link Function. A link function is simply a function of the mean of the response variable Y that we use as the response instead of Y itself. All that means is when Y is categorical, we use the logit of Y as the response in our regression equation instead of just Y: The logit function is the natural log of the odds that Y equals one of the categories. For mathematical simplicity, we’re going to assume Y has only two categories and code them as 0 and 1.
    Output:
    