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
connection, disable any ad blockers, or try using a different browser.
</reference>

<statements>
1. and sports-specific evidence remains short-term, school-based, vendor-reported or benchmark-only
2. The practical conclusion is that construction is feasible and partially operational, while application claims require stronger independent validation of device validity, privacy, usability, teacher integration and longitudinal learning outcomes
3. A systematic review of deep-learning HPE in sport categorizes applications into movement skill analysis, action recognition, augmented coaching tools and officiating support
4. It finds that bespoke multi-model algorithms are common and single-person pose estimation predominates
5. Of 50 included studies, only 12 validated algorithms on live human participants, while 38 used datasets only
6. Private datasets were used for training and validation in 21 studies, limiting reproducibility and generalizability
7. The review also states that systems trained in laboratory-like environments may perform poorly in real settings
8. This human-in-the-loop design is important because one HPE review warns that augmented coaching tools may remove the need for a coach, which could have long-term impacts on performance
9. The HPE review identifies athlete development, movement skill analysis, action recognition, coaching tools and officiating as domains
10. These are vendor-reported deployment outcomes, not independent controlled efficacy estimates, and they should be read alongside the review finding that no longitudinal studies empirically test the long-term effects of DL HPE tools in athlete development programs
11. a systematic review finds no longitudinal efficacy studies for DL pose estimation in sport
12. The available evidence does not establish durable learning or health gains at scale, because the cited evidence base lacks longitudinal sports ITS efficacy studies and relies heavily on short-term, quasi-experimental, vendor or benchmark reports
13. The HPE review states that most studies rely on private datasets, limiting reproducibility and generalizability, and that lack of open datasets and standardized practices poses challenges for adoption
14. The HPE review states that most studies rely on private datasets, limiting reproducibility and generalizability, and that lack of open datasets and standardized practices poses challenges for adoption. It also notes that algorithmic metrics are often inaccessible and lack direct practical interpretation for sports-science practitioners
15. The HPE review notes that lab-trained systems may fail in real settings and that multi-person tracking remains difficult
16. The HPE review warns that automated coaching tools may remove the coach, with possible long-term performance impacts
17. The HPE review notes that single-person focus removes contextual factors important in many sports
18. Whether multimodal sports tutoring produces durable skill, health and motivation gains beyond short-term classroom effects is not settled by the cited evidence
19. Whether existing sensor and ITS evaluation methods can be harmonized into a common validation protocol remains open, given calls for device validity, standardized practices and longer interventions
20. A defensible next step is to combine the cloud–edge–end architecture, the digital-base and platform layers, deployed gateway stacks and coaching-generation models under a validation regime that includes device concurrent validity, privacy and security governance, teacher-centered evaluation
21. A defensible next step is to combine the cloud–edge–end architecture, the digital-base and platform layers, deployed gateway stacks and coaching-generation models under a validation regime that includes device concurrent validity, privacy and security governance, teacher-centered evaluation, open or standardized benchmarks
</statements>

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