Temkin - Wikimedia disambiguation page. Article "Темкин" in Russian Wikipedia has 12.7 points for quality (as of August 1, 2024). The article contains 1 references and 2 sections.
Since the creation of article "Темкин", its content was written by 4 registered users of Russian Wikipedia and edited by 4 registered Wikipedia users in all languages.
The article is cited 3 times in Russian Wikipedia and cited 3 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 | Russian: #293645 03.2010 |
Global: #4543490 03.2010 |
| Best Rank | Russian: #61920 02.2021 |
Global: #1525267 06.2008 |
On 12 November 2024 in multilingual Wikipedia, Internet users most often read articles on the following topics: Donald Trump, Marco Rubio, Elon Musk, Song Jae-lim, 2024 United States presidential election, Megan Fox, Melania Trump, Gladiator II, ChatGPT, deaths in 2024.
In Russian Wikipedia the most popular articles on that day were: Яндекс, Трамп, Дональд, YouTube, Президентские выборы в США (2024), Молодёжка. Студенты, Трамп, Мелания, Чайлдфри, Субстанция (фильм), Список умерших в 2024 году, Россия.
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
$$Quality=1/c∑↙{i=1}↖c nm_i-RS=1/5(1.06+0.55+0+10+51.83)$$
where:
The results of the calculations may be slightly different because of rounded values of normalized metrics in formulas
