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>
ScienceDirect



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Reference number:
a3bea74f4d4c106c
IP Address:
208.110.231.2
User Agent:
Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) HeadlessChrome/153.0.0.0 Safari/537.36
Timestamp:
2026-09-16 08:55:40 UTC
CPE00001
::CLOUDFLARE_ERROR_1000S_BOX::
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</reference>

<statements>
1. Maize, soybean, wheat, and other crops have been successfully reconstructed using multi-view RGB imaging with SfM–MVS, often with sub-millimeter accuracy for plant height, leaf dimensions, and canopy traits. Similar workflows are applied to grain- or panicle-level traits, for example in seed reconstruction settings or specialized platforms like Seedscreener.
2. Multi-sensor systems—including RGB cameras, depth sensors, LiDAR, and NIR—are increasingly used in plant phenotyping. Kalman filters and their nonlinear variants (EKF, UKF) offer natural frameworks to fuse these modalities.
3. For grain phenotyping platforms like Seedscreener, NIR features and 3D shape reconstruction can be fused to improve classification of germplasm and estimate seed composition traits.
4. Optimal control problems can be solved to choose sampling angles and speeds that minimize uncertainty while respecting throughput constraints, using stochastic quality metrics derived from state-space models. Learned observers (NeRF/3DGS) then reconstruct seed geometry, and trait extraction functions compute length, width, thickness, volume, and shape descriptors following volume-carving or neural pipelines.
5. Build multi-sensor fusion through Kalman/Bayesian filters where applicable.
</statements>

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