The quality of encyclopedia articles is a core concern for Wikipedians. Currently, some of the large Wikipedia'sWikipedias roughly follow the [[:en:WP:Wikipedia 1.0|Wikipedia 1.0]] assessment rating scale when evaluating the quality of articles. Having these assessments is very useful, because it helps us gauge our progress and identify missed opportunities (e.g. popular articles that are low quality). However, keeping these assessments up to date is challenging, so coverage is inconsistent. This is where the <code>wp10</code> machine learning model comes in handy. By training a model to replicate the article quality assessments that humans perform, we can automatically assess every article and every revision with a computer. This model has been used to help Wikiprojects triage re-assessment work and to explore the editing dynamics that lead to article quality improvements.
*<code>[[ORES/wp10|wp10]]</code> -- predicts the Wikipedia 1.0 assessment class of an article or draft
