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Chances are, right now, you have a Gregorian calendar stuck to your wall.

This calendar, with the months January through December, is a business standard used in many places round the world to define the year: one which hearkens back to Christian and Roman Imperial precedents.

For example, the results of dendrochronology (tree-ring) analysis may tell us that a particular roof beam was from a tree chopped down in A. For example, the stratum, or layer, in which an artifact is found in an ancient structure may make it clear that the artifact was deposited sometime after people stopped living in the structure but before the roof collapsed.

However, the stratigraphic position alone cannot tell us the exact date.

This uses the decay of carbon and the ratio of C-12 to C-14 to determine the age of the artifact. It is also not very accurate for more recent artifacts.

Using all of the information about the artifacts, including context, typology, dating and more, archaeologists can piece together the events and culture of past society.

But what does it actually do and how much can it tell us?

Radiocarbon dating is a side benefit of a naturally occurring scientific process.

This can be done using artifacts found in the site with known dates. They also can be used to determine the culture the artifacts are from.An example is a frequently used fire ring will leave evidence behind in the soil, but it cannot be moved with the occupants.After archaeologists have excavated the site completely, or to the extent the project planned, they fill the site back in and take the artifacts to be analyzed.Dates could be assigned based upon scientific evidence rather than on the inconsistent mathematics, historical comparisons and simulated typologies of artifacts that had previously regulated time.The most well known and oft used form of radiometric dating is radiocarbon dating. It has helped define the ages of man in ways never thought possible and led the way for a vanguard of scientific techniques that have further defined time for humanity and beyond.

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  1. Self-organizing maps differ from other artificial neural networks as they apply competitive learning as opposed to error-correction learning (such as backpropagation with gradient descent), and in the sense that they use a neighborhood function to preserve the topological properties of the input space. The first plot shows the grouping when the data are split into two clusters.