Article "吴维尊" in Chinese Wikipedia has 7.4 points for quality (as of August 1, 2024). The article contains 1 references and 2 sections. The article also contains quality flaw template, which reduce quality score.
Since the creation of article "吴维尊", its content was written by 9 registered users of Chinese Wikipedia and edited by 9 registered Wikipedia users in all languages.
The article is cited 7 times in Chinese Wikipedia and cited 7 times in all languages.
The highest Authors Interest rank from 2001:
The highest popularity rank from 2008:
There is 1 language version for this article in the WikiRank database (of the considered 55 Wikipedia language editions).
The quality and popularity assessment was based on Wikipédia dumps from August 1, 2024 (including revision history and pageviews for previous years).
| Best Rank | Chinese: #102766 09.2009 |
Global: #2989229 03.2008 |
| Best Rank | Chinese: #31845 05.2015 |
Global: #1412083 08.2007 |
On 3 November 2024 in multilingual Wikipedia, Internet users most often read articles on the following topics: 2024 United States presidential election, Kamala Harris, Donald Trump, 2024 Formula One World Championship, The Substance, Kemi Badenoch, deaths in 2024, Elon Musk, Venom: The Last Dance, Max Verstappen.
In Chinese Wikipedia the most popular articles on that day were: Faker, 来自地狱的法官, 2024年美國總統選舉, 英雄联盟全球总决赛, 永夜星河, T1 (電競隊伍), 劉德華, 焚城, BLG电子竞技俱乐部, 杜琪峯.
The WikiRank project is intended for automatic relative evaluation of the articles in the various language versions of Wikipedia. At the moment the service allows to compare over 44 million Wikipedia articles in 55 languages. Quality scores of articles are based on Wikipedia dumps from August, 2024. When calculating current popularity and AI of articles data from July 2024 was taken into account. For historical values of popularity and AI WikiRank used data from 2001 to 2023... More information
$$Quality=1/c∑↙{i=1}↖c nm_i-RS=1/5(7.59+1.01+0+16.67+13.53)-0.39$$
where:
The results of the calculations may be slightly different because of rounded values of normalized metrics in formulas
