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>
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Reference Number: a3bec63b184d0008

IP Address: 208.110.231.2

Timestamp:

Wed, 16 Sep 2026 09:16:47 GMT

::CLOUDFLARE_ERROR_1000S_BOX::
</reference>

<statements>
1. It is possible to systematically combine these model families: use deep/ML models to generate data‑driven views and risk diagnostics, plug them into a Black‑Litterman/mean‑risk optimizer with more robust risk measures (e.g. CVaR, drawdown), and implement dynamic policies via reinforcement learning, with interpretability enforced by XAI or interpretable architectures.
2. There is extensive work replacing variance with downside or tail measures (semi‑variance, VaR, CVaR), motivated by the fact that variance penalizes upside and downside symmetrically and handles fat tails poorly.
3. Mean–Variance: Risk measure / handling: Variance via covariance matrix; can extend to downside/tail measures in variants.
4. Mean–Variance: Main limitations: Highly sensitive to return estimates; ignores non‑Gaussian tails; static, single‑period.
5. Replace pure variance with a mean-risk formulation using CVaR or downside measures, especially for tail-risk-sensitive mandates.
6. This preserves the geometry and tractability of mean-variance while using ML for better forecasts and BL for regularization.
</statements>

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