You are required to act as an answer evaluator. Given an issue context, the hint disclosed to the agent and the answer from the agent, 
you should rate the performance of the agent into three levels: "failed", "partially", and "success". The rating rules are as follows:

<rules>
1. You will be given a list of <metrics>, for each metric, you should rate in [0,1] for the agent based on the metric criteria, and then multiply the rating by the weight of the metric.
2. If the sum of the ratings is less than 0.45, then the agent is rated as "failed"; if the sum of the ratings is greater than or equal to 0.45 and less than 0.85, then the agent is rated as "partially"; if the sum of the ratings is greater than or equal to 0.85, then the agent is rated as "success".
3. **<text>** means the text is important and should be paid attention to.
4. ****<text>**** means the text is the most important and should be paid attention to.
</rules>

The <metrics> are as follows:
{
    "m1": {
        "criteria": "Precise Contextual Evidence:
            1. The agent must accurately identify and focus on the specific issue mentioned in the context. This involves a close examination of the exact evidence given and determining whether it aligns with the content described in the issue and the involved files.
            2. Always ask yourself: Have the agent provided correct and detailed context evidence to support its finding of issues? If the agent just gives some general description without specifically pointing out where the issues occur, you should give it a low rate.
            3. Once the agent has correctly spotted **** all the issues in <issue> and provided accurate context evidence ****, it should be given a ****full score (1.0) even if it includes other unrelated issues/examples ****"
            4. If the agent has only spotted part of the issues with the relevant context in <issue>, then you should give a medium rate.
            5. The expression in the answer of the agent might not directly pinpoint the issue, but when its answer implies the existence of the <issue> and has provided correct evidence context, then it should be given a high rate for m1.
            6. For issues about something missing and having no clear location information, even if there is context in <issue> involved files, it's ok for the agent to only give an issue description without pointing out where the issue occurs in detail."
         "weight": 0.8,
        "range": [0, 1]
    },
    "m2": {
        "criteria": "Detailed Issue Analysis: 
            1. The agent must provide a detailed analysis of the issue, showing an understanding of how this specific issue could impact the overall task or dataset as human evaluators do.
            2. This metric stresses the importance of not just identifying that there is an issue but also understanding and explaining its implications in detail, rather than simply repeating the information in hint.",
        "weight": 0.15,
        "range": [0, 1]
    },
    "m3": {
        "criteria": "Relevance of Reasoning: 
            1. The agent’s reasoning should directly relate to the specific issue mentioned, highlighting the potential consequences or impacts.
            2. This metric ensures that the agent’s logical reasoning directly applies to the problem at hand, rather than being a generic statement.",
        "weight": 0.05,
        "range": [0, 1]
    }
}
</metrics>

--------------------------------

Now let’s begin:

<issue>
title: Issues with data in the college-majors repo
content: I think something went wrong during the data processing, the columns Men and Women somehow became misaligned, but I was able to match them up again by matching rows where Men + Women == Total.

    Thankfully, the rest of the data seems to be aligned properly. Please look at the original data source and verify that the data has been aligned properly now.
involved: [
    {
        "name": "recent-grads.csv",
        "context": "All rows from the "recent-grads.csv" file where the values in the "Total" column are not equal to the sum of the values in the "Men" and "Women" columns."
    }
]
</issue>

<hint>
data misalignment issue
</hint>

-------------------- Below is the answer from the agent. Ensure you don’t take the information above as the agent’s answer!

<answer>
I will examine the datasets to identify any data misalignment issues. Data misalignment issues in CSV files can include misaligned columns, missing headers, and irregular row lengths. Here are the issues I found:

### Issue 1

```json
{
    "issue": "Data misalignment in `recent-grads.csv`",
    "evidence": "Row 3: '0 50 660' instead of '0.050661', '65 000.0' instead of '65000.0'",
    "description": "In the `recent-grads.csv` file, the values in row 3 are misaligned. Instead of '0.050661', it is displayed as '0 50 660', and '65000.0' is displayed as '65 000.0'. This misalignment indicates that the data is not correctly parsed, likely due to incorrect delimiters or spaces in the original data."
}
```

### Issue 2

```json
{
    "issue": "Missing header in `all-ages.csv`",
    "evidence": "Row 0: 'All ages Bachelor's degree 448564 45199 ...'",
    "description": "The first row in the `all-ages.csv` file appears to contain actual data instead of column headers. This suggests that the header row is missing, causing potential misalignment when interpreting the dataset."
}
```

### Issue 3

```json
{
    "issue": "Inconsistent row lengths in `grad-students.csv`",
    "evidence": "Row 5: '15,21 45211 ...' (20 columns), Row 10: '14,20 42013 ...' (22 columns)",
    "description": "The `grad-students.csv` file has inconsistent row lengths. Row 5 has 20 columns, while row 10 has 22 columns. This inconsistency can lead to difficulties in data analysis and processing, as it indicates irregularities in data entry or formatting."
}
```

These issues highlight potential problems in the dataset files that need to be addressed to ensure data integrity and proper analysis.
</answer>

-------------------- 

response below, 
1. after your analysis, remember to give a **"decision: [failed/partially/success]"** for me to extract it using REGEX.
2. Don't use Code Interpreter!; Use your ability to analyze the text.  ** Pay attention to your calculations and make sure they are correct. **
3. There could be multiple issues described in <issue> part. You should start by thinking clearly about how many issues exist in <issue> and list them out, then you should compare them with the answer from the agent.
4. you should focus on whether the agent has spotted the issue in <issue> rather than caring about whether the agent includes unrelated examples not present in the context.
