You will be provided with a reference and some statements. Please determine whether each statement is 'supported', 'unsupported', or 'unknown' with respect to the reference. Please note:
First, assess whether the reference contains any valid content. If the reference contains no valid information, such as a 'page not found' message, then all statements should be considered 'unknown'.
If the reference is valid, for a given statement: if the facts or data it contains can be found entirely or partially within the reference, it is considered 'supported' (data accepts rounding); if all facts and data in the statement cannot be found in the reference, it is considered 'unsupported'.

You should return the result in a JSON list format, where each item in the list contains the statement's index and the judgment result, for example:
[
    {
        "idx": 1,
        "result": "supported"
    },
    {
        "idx": 2,
        "result": "unsupported"
    }
]

Below are the reference and statements:
<reference>
ResearchGate - Temporarily Unavailable



Access restricted
We've detected unusual activity from your network. Access to this page is temporarily restricted.
Are you a researcher?
Log in or sign up as a ResearchGate member to verify your access.
Log in
Create account
Ray ID: a3beb537b91bfa21
Client IP: 208.110.231.2
©
2008-2026
ResearchGate GmbH. All rights reserved.
</reference>

<statements>
1. In the benchmark dataset comparison, FineDiving is listed as a benchmark dataset for action quality assessment.
2. FineDiving evaluates competitive springboard and platform diving.
3. FineDiving uses high-definition RGB video and 3D pose data as input modalities.
4. FineDiving includes action units, sub-action temporal boundary frames, and official FINA competition scores.
5. The primary performance baseline for FineDiving is 0.88–0.91 for pose-guided contrastive pipelines.
6. In the benchmark dataset comparison, MTL-AQA is listed as a benchmark dataset for action quality assessment.
7. MTL-AQA evaluates Olympic diving disciplines including 10m platform and 3m springboard.
8. MTL-AQA uses multi-angle RGB video feeds as input modalities.
9. MTL-AQA includes difficulty ratings, multi-judge scores, action classes, and technical commentary text.
10. In the benchmark dataset comparison, Fis-V is listed as a benchmark dataset for action quality assessment.
11. Fis-V evaluates figure skating short and free skating programs.
12. Fis-V uses RGB video streams and acoustic audio tracks as input modalities.
13. Fis-V includes total scores, total element scores, and program component scores.
</statements>

Begin the assessment now. Output only the JSON list, without any conversational text or explanations.