It probably does well when translating general human speech, but I was rather interested in its performance on a paragraph of a scientific text (it being a much

It probably does well when translating general human speech, but I was rather interested in its performance on a paragraph of a scientific text (it being a much more challenging task, obviously). * "The impact of the solar wind protons on the surface of Mercury" became "Der Einfluss der solaren Windprotonen auf die Oberfläche von Quecksilber". Note that 'solar wind protons' should have been translated as 'Sonnenwindprotonen' instead, i.e. the word 'solar' was to be a part of the noun's modifier, but it was pushed out. * The lack of domain-specific training is especially obvious with the case of the planet's name being translated as "Quecksilber" instead of "Merkur" (Quecksilber being the name of the metal). * "pure northward interplanetary magnetic field (IMF)" became "reines interplanetares interplanetares Magnetfeld nach Norden (IWF)". Aside from this being a poor translation, it's worth noting that DeepL didn't properly process the introduction of an abbreviation (IWF being the abbrev. for the International Monetary Fund in German).