Scientists in ScienceAdvances have developed a machine learning approach to identify regions of Antarctica that may harbor undiscovered meteorites.
), three thresholds are investigated: 100, 150, and 200 g, resulting in 1179, 975, and 825 observations, respectively. In all three cases, the performance of the classifier does not change, indicating that wind displaced meteorites do not influence the classifier and/or that there are few observations of meteorites displaced over larger distances .
Future improvements of the MSZ prediction depend on the quality and availability of observations. The classifier tends toward underfitting , indicating that additional features are likely to improve the performance of the classifier even further. Moreover, regarding the set of positive observations, only a third of more than 45,000 Antarctic meteorite finds has been used for the classification, as no detailed location data are available for the other meteorites.
The classified 613 MSZs distributed over the entire Antarctic continent correspond to both known and unexplored MSZs. The fact that the classification successfully identifies over 80% of the known MSZs gives us a high confidence in the classifier’s prediction of unexplored MSZs.
By combining estimates on the total of true-positive and false-negative observations , the precision (
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