railf.jp

Japan Railfan Magazine is a Japanese-language monthly magazine for railfans covering the mainly Japanese railways published by Koyusha. It has been published in Japan since 1961. Issues go on sale on the 21st of each month, two months before the cover month. Each copy sells for between ¥1,100 and ¥1,200 depending on the number of pages. The magazine reports on railway prototypes, complete with technical plans, photos, maps, graphs, and tables. More information...

Multilingual Wikipedia

In June 2020 the website railf.jp was on the 2,080th place in the ranking of the most reliable and popular sources in multilingual Wikipedia from readers' point of view (PR-score). If we consider only frequency of appearance of this source in references of Wikipedia articles (F-score), this website was on the 2,390th place in June 2020. From Wikipedians' point of view, "railf.jp" is the 2,306th most reliable source in different language versions of Wikipedia (AR-score).

The website is placed before madman.com.au and after accesshollywood.com in multilingual PR ranking of the most reliable sources in Wikipedia.

PR-score:
2,080th place
17,245,888
-2,440,604
AR-score:
2,306th place
2,184,669
+16,691
F-score:
2,390th place
6,215
+70

Japanese Wikipedia (ja)

PR-score:
141st place
15,462,052
-2,087,956
AR-score:
98th place
1,803,249
+12,252
F-score:
133rd place
4,575
+53

English Wikipedia (en)

PR-score:
9,777th place
1,490,962
-322,428
AR-score:
6,178th place
294,090
+2,044
F-score:
4,320th place
1,029
+3

Chinese Wikipedia (zh)

PR-score:
3,981st place
238,098
-23,725
AR-score:
2,166th place
37,057
+1,845
F-score:
1,199th place
330
+10

Korean Wikipedia (ko)

PR-score:
5,869th place
14,363
-4,319
AR-score:
1,086th place
20,944
+172
F-score:
573rd place
157
0

French Wikipedia (fr)

PR-score:
62,953rd place
11,884
-2,030
AR-score:
15,441st place
12,968
+4
F-score:
21,824th place
24
0
Show all Wikipedia languages...

BestRef shows popularity and reliability scores for sources in references of Wikipedia articles in different languages. Data extraction based on complex method using Wikimedia dumps. To find the most popular and reliable sources we used information about over 200 million references of Wikipedia articles. More details...