Dr. Max Fomitchev-Zamilov

I would say 10 times.

I would say 10 times.

And you can actually see it here from numbers. You see the sort of the center of the bottom ellipse is one on the left, and the center of the upper circle is 10. So that's how far apart they are about 10 times. And the same true about the horizontal axis.

And you can actually see it here from numbers. You see the sort of the center of the bottom ellipse is one on the left, and the center of the upper circle is 10. So that's how far apart they are about 10 times. And the same true about the horizontal axis.

And you can actually see it here from numbers. You see the sort of the center of the bottom ellipse is one on the left, and the center of the upper circle is 10. So that's how far apart they are about 10 times. And the same true about the horizontal axis.

So the next plot is just a summary of the quality metric. So on the previous plot, there were like two subsets of the quality metrics. It's concentricity and circularity of the outer surface. So this particular chart is more of a comprehensive quality metric that also includes the inner surface. So it's concentricity and circularity of both inner and outer.

So the next plot is just a summary of the quality metric. So on the previous plot, there were like two subsets of the quality metrics. It's concentricity and circularity of the outer surface. So this particular chart is more of a comprehensive quality metric that also includes the inner surface. So it's concentricity and circularity of both inner and outer.

So the next plot is just a summary of the quality metric. So on the previous plot, there were like two subsets of the quality metrics. It's concentricity and circularity of the outer surface. So this particular chart is more of a comprehensive quality metric that also includes the inner surface. So it's concentricity and circularity of both inner and outer.

And also the error of the, let's say, the inner surface relative to the outer surface. Because you can have both of them, you know, perfectly, but if you hollowed out your vase incorrectly, then there will be an offset. And of course, that's a telltale mark of a manual labor. You know, when you hollow something out, how do you know where the center is? You know, you eyeball it.

And also the error of the, let's say, the inner surface relative to the outer surface. Because you can have both of them, you know, perfectly, but if you hollowed out your vase incorrectly, then there will be an offset. And of course, that's a telltale mark of a manual labor. You know, when you hollow something out, how do you know where the center is? You know, you eyeball it.

And also the error of the, let's say, the inner surface relative to the outer surface. Because you can have both of them, you know, perfectly, but if you hollowed out your vase incorrectly, then there will be an offset. And of course, that's a telltale mark of a manual labor. You know, when you hollow something out, how do you know where the center is? You know, you eyeball it.

So chances of you hollowing out something, let's say it was a 10 micron precision, and that's what, you know, half a thousandth of an inch, it's 10, you know, 12 microns. Chances of you doing that, you know, next to nothing, right? And much less on a bunch of artifacts. So this particular plot includes, you know, all of this consideration. So it magnifies the gap between quality classes even more.

So chances of you hollowing out something, let's say it was a 10 micron precision, and that's what, you know, half a thousandth of an inch, it's 10, you know, 12 microns. Chances of you doing that, you know, next to nothing, right? And much less on a bunch of artifacts. So this particular plot includes, you know, all of this consideration. So it magnifies the gap between quality classes even more.

So chances of you hollowing out something, let's say it was a 10 micron precision, and that's what, you know, half a thousandth of an inch, it's 10, you know, 12 microns. Chances of you doing that, you know, next to nothing, right? And much less on a bunch of artifacts. So this particular plot includes, you know, all of this consideration. So it magnifies the gap between quality classes even more.

And this is where I draw the line. You know, I just draw the line arbitrarily, say, you know, the quality metric of 25 is of my choosing, you know, the boundary between the precise versus the imprecise class. And below that, all of the, half of the vases that I analyzed from your collection, and the one I'm holding here, and three of the modern artifacts.

And this is where I draw the line. You know, I just draw the line arbitrarily, say, you know, the quality metric of 25 is of my choosing, you know, the boundary between the precise versus the imprecise class. And below that, all of the, half of the vases that I analyzed from your collection, and the one I'm holding here, and three of the modern artifacts.

And this is where I draw the line. You know, I just draw the line arbitrarily, say, you know, the quality metric of 25 is of my choosing, you know, the boundary between the precise versus the imprecise class. And below that, all of the, half of the vases that I analyzed from your collection, and the one I'm holding here, and three of the modern artifacts.

And above it, the other half of the vases from your collection and two of the handmade artifacts. And what this chart alone tells me is, according to the analysis method that I chose, Half objects in your collection are perfectly consistent with handmade vases.

And above it, the other half of the vases from your collection and two of the handmade artifacts. And what this chart alone tells me is, according to the analysis method that I chose, Half objects in your collection are perfectly consistent with handmade vases.

And above it, the other half of the vases from your collection and two of the handmade artifacts. And what this chart alone tells me is, according to the analysis method that I chose, Half objects in your collection are perfectly consistent with handmade vases.