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':1091522
'query':population based incremental learning (pbil)
'pid':7665479
'passage':There are two types of logical extraction methods: Full Extraction: Full extraction is used when the data needs to be extracted and loaded for the first time. In full extraction, the data from the source is extracted completely. This extraction reflects the current data available in the source system. Incremental Extraction: In incremental extraction, the changes in source data need to be tracked since the last successful extraction. Only these changes in data will be extracted and then loaded. These changes can be detected from the source data which have the last changed timestamp.
    Output:
    