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: Missing task_<task_type>.json in uploaded files according to the contribution guidelines
content: This dataset mentioned a task in README file but haven't provide a task_<task_type>.json according to GLI guideline
involved: [
    {
        "name": "DATASET_SUBMISSION.md",
        "context": "there are many relevant contexts in this file where imply there should be a task_<task_type>.json file. Mentioning any of them is enough."
    }
]
</issue>

<hint>
missing required JSON file according to contribution guidelines
</hint>

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

<answer>
Based on the contents of the `README.md` and `DATASET_SUBMISSION.md` files, here are the identified issues:

1. **Issue: Missing task configuration file**
    - **Evidence**: "A task configuration file that stores an available task on the given dataset. See [GLI Task Format](./FORMAT.md#gli-task-format)."
    - **Description**: According to the `DATASET_SUBMISSION.md` guidelines, each dataset submission must include a task configuration file (e.g., `task_<task_type>.json`). The `README.md` file references a Jupyter notebook for preprocessing but does not mention any task configuration file, indicating that it is missing from the dataset submission.

2. **Issue: Incomplete description in README.md**
    - **Evidence**: "Check MUTAG.ipynb for the preprocessing."
    - **Description**: The `README.md` file mentions checking `MUTAG.ipynb` for preprocessing details but does not provide a summary or any description of the preprocessing steps within the `README.md` itself. Including a brief description of the preprocessing steps in the `README.md` would provide more context and make the documentation more complete.

3. **Issue: Inconsistent citation format**
    - **Evidence**: 
      ```markdown
      `@article{Debnath1991StructureactivityRO,
        title={Structure-activity relationship of mutagenic aromatic and heteroaromatic nitro compounds. Correlation with molecular orbital energies and hydrophobicity.},
        author={Asim Kumar Debnath and R L Compadre and Gargi Debnath and Alan J. Shusterman and Corwin Hansch},
        journal={Journal of medicinal chemistry},
        year={1991},
        volume={34 2},
        pages={
          786-97
        },
        url={https://api.semanticscholar.org/CorpusID:19990980}`
      ```
    - **Description**: The citation in the `README.md` uses backticks for formatting. This could lead to formatting issues when rendered. Instead, the citation should use standard Markdown or plain text to ensure consistency and readability.

These issues should be addressed to ensure the dataset submission aligns with the guidelines provided in `DATASET_SUBMISSION.md` and that the documentation is complete and consistent.
</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.
