Thanks. From the article, for anyone else skimming who had the same question:

    Technical reproduction. The Vesuvius Challenge Technical Review Team reproduced the winning submissions manually. We made sure to clearly understand every part of the code, and that when we run it independently we get similar output images. Since all code and training data is now open source, you can do the same!
    Multiple submissions of the same area. You might have noticed that all submission images above show the same area of the scroll. This is because we released 3d-mapped papyrus sheets within the CT-scan (“segments”) created by our segmentation team, which were then used by all contestants. The resulting output images — created by different ML models and training labels — have produced extremely similar results. This holds not just for the winners and runner ups, but also for the other submissions that we received.
    Small input/output windows. The ink detection models are not based on Greek letters, optical character recognition (OCR), or language models. Instead, they independently detect tiny spots of ink in the CT scan, the writing appearing later when these are aggregated. As a result, the text appearing in the images is not the imagined output of a machine learning model, but is instead directly tied to the underlying data in the CT scan.