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>
Client Challenge



A required part of this site couldn’t load. This may be due to a browser
extension, network issues, or browser settings. Please check your
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</reference>

<statements>
1. A 2026 hierarchical IMU+expert-rubric fusion model achieved 91.3±0.4% accuracy, "significantly surpass[ed] single-modality baselines (p < 1×10⁻⁸)," stayed stable under simulated sensor failure, and ran at 43.2 ms/sample—classroom-ready.
2. The recurring problem they solve is that conventional assessment "depends on subjective teacher observation and simple rating scales, often resulting in subjective bias, low evaluation efficiency, and delayed instructional feedback"—acute in large classes.
3. Modalities are complementary, redundant, and cooperative; cross-modal attention learns alignment; contrastive learning aligns semantics in a shared latent space; adaptive gated fusion reweights modalities by data quality when a sensor drops or a camera is occluded.
4. A 2026 PE-assessment study framed such systems as ones that "intelligently reproduce expert-level teacher scoring rather than objective biomechanical measurement," making them "a powerful auxiliary tool for classroom teaching rather than a substitute for professional teacher judgment."
5. The 2026 PE-assessment framework used DTW "for spatiotemporal alignment to resolve asynchrony between continuous sensor signals and discrete manual scoring."
6. The 2026 IMU+rubric model "significantly surpass[ed] single-modality baselines (p < 1×10⁻⁸)," held up "under simulated data missingness and sensor failure," and ablations "verified the essential role of cross-modal attention."
</statements>

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