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: many urls that are clearly benign are marked as malicous
content: Why are websites like www.python.org/community/jobs/ and www.apache.org/licenses/ marked as phishing?
involved: [
    {
        "name": "malicious_phish.csv",
        "context": "urls that are clearly benign are marked as malicous:
www.gnu.org/people/speakers.html,phishing
www.bbc.co.uk/opensource/,phishing
www.ibm.com/developerworks/opensource,phishing
www.phpbuilder.com/people/,phishing
www.python.org/community/jobs/,phishing
www.opensource.org/licenses/,phishing
www.affero.org/oagpl.html,phishing
www.opensource.org/licenses/artistic-license.php,phishing
www.mozilla.org/MPL/,phishing
www.oreilly.com/catalog/osfreesoft/book/,phishing
www.php.net/license/,phishing
www.apache.org/licenses/,phishing
www.mozilla.org/MPL/MPL-1.0.html,phishing
www.latex-project.org/lppl/,phishing
"
    }
]
</issue>

<hint>
None
</hint>

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

<answer>
Here are the potential mislabeling issues identified in the dataset and documentation:

### Issue 1
{
    “issue”: “Mislabeling of URL types in the dataset”, 
    “evidence”: ”
| url                                                | type       |
|----------------------------------------------------|------------|
| br-icloud.com.br                                   | phishing   |
| mp3raid.com/music/krizz_kaliko.html                | benign     |
| bopsecrets.org/rexroth/cr/1.htm                    | benign     |
| http://www.garage-pirenne.be/index.php?option=... | defacement |
| http://adventure-nicaragua.net/index.php?optio... | defacement |
    “, 
    “description”: “The types of URLs in the dataset seem to be mislabeled. For example, 'br-icloud.com.br' might be an indicator of a phishing URL, but 'mp3raid.com/music/krizz_kaliko.html' and 'bopsecrets.org/rexroth/cr/1.htm' are marked as benign. It's crucial to re-evaluate the URLs to ensure correct labeling.” 
}

### Issue 2
{
    “issue”: “Inconsistent data sources mentioned in datacard.md”, 
    “evidence”: ” 
## Content
we have collected a huge dataset of 651,191 URLs, out of which 428103 benign or safe URLs, 96457 defacement URLs, 94111 phishing URLs, and 32520 malware URLs. Figure 2 depicts their distribution in terms of percentage. As we know one of the most crucial tasks is to curate the dataset for a machine learning project. We have curated this dataset from five different sources.

For collecting benign, phishing, malware and defacement URLs we have used URL dataset (ISCX-URL-2016) For increasing phishing and malware URLs, we have used Malware domain black list dataset. We have increased benign URLs using faizan git repo At last, we have increased more number of phishing URLs using Phishtank dataset and PhishStorm dataset As we have told you that dataset is collected from different sources. So firstly, we have collected the URLs from different sources into a separate data frame and finally merge them to retain only URLs and their class type.”, 
    “description”: “The description of data sources in 'datacard.md' is inconsistent and lacks clarity. For instance, the phrase 'For collecting benign, phishing, malware and defacement URLs we have used URL dataset (ISCX-URL-2016) For increasing phishing and malware URLs, we have used Malware domain black list dataset.' does not clearly specify which parts of the data were obtained from each source. Clear and consistent documentation is essential for data verification and reproducibility.” 
}

### Issue 3
{
    “issue”: “Typographical errors in datacard.md”, 
    “evidence”: ” 
Malicious URLs host unsolicited content (spam, phishing, drive-by downloads, etc.) and lure unsuspecting users to become victims of scams (monetary loss, theft of private information, and malware installation), and cause losses of billions of dollars every year. We have collected this dataset to include a large number of examples of Malicious URLs so that a machine learning-based model can be developed to identify malicious urls so that we can stop them in advance before infecting computer system or spreading through inteinternet.

we have collected a huge dataset of 651,191 URLs, out of which 428103 benign or safe URLs, 96457 defacement URLs, 94111 phishing URLs, and 32520 malware URLs. Figure 2 depicts their distribution in terms of percentage. As we know one of the most crucial tasks is to curate the dataset for a machine learning project. We have curated this dataset from five different sources.”
, 
    “description”: “There are typographical errors in 'datacard.md'. For example, the word 'inteinternet' should be 'internet'. Such errors can undermine the credibility of the documentation and should be corrected to maintain professionalism.” 
}
</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.
