Wikimedia Apps/Suggested edits/zh

欢迎来到编辑建议
编辑建议是在Android设备上编辑维基百科的一个新途径. 我们感谢您的尝试.

什么是编辑建议？
编辑建议为维基百科提供了微小但却不可或缺的贡献机会. 我们希望让大家认识到人人都可编辑维基百科且每个人都能简单轻易的做出贡献.

开始使用编辑建议功能
编辑建议主页面由以下区域组成：个人数据/贡献历史和贡献机会. 贡献机会是编辑建议的核心部分. 在这里，你可以找到为维基百科做出贡献的途径. 目前我们提供添加或翻译条目描述和图像说明的机会. 如果你想了解更多或学习如何像专家一样进行编辑，请查看下面的专门章节：



我们将很快推进更多贡献方式，敬请期待.

个人数据显示了你在编辑建议中的活动数据. They start to populate once you've made your first Suggested edits.

Tapping the card leads to the contribution history page. The contribution history lists all contribution types that are available in Suggested edits. You can filter by edit type and see how many pageviews the articles you’ve contributed to had in the past 30 days. Tapping an item in the contribution history list leads you the edit detail (diff) page, where even more infos about the particular edit are featured.
 * Contributions - displays the number of contributions you’ve made with Suggested edits in the past 30 days.
 * Pageviews - displays the total number of times in the last 30 days that others viewed items you contributed to using Suggested edits.
 * Edit streak - displays how many days without a break you’ve contributed via Suggested edits. If you haven’t contributed in a while, it shows your last contribution date.
 * Edit quality - based on how many times one of your contributions was reverted (that is: undone by another editor). The fewer reverted contributions, the better the edit quality.

什么是条目描述？

 * Short, multilingual descriptions of items (e.g. Wikipedia articles)
 * Article descriptions are not full sentences, but small bits of information.
 * In most cases, the proper length is between two and twelve words.

条目描述有什么用？
Article descriptions are shown in the apps below Wikipedia article titles to help readers identify the article they're looking for. Article descriptions are stored and maintained on Wikidata and have been designed to disambiguate items with the same or similar labels. Descriptions are also shown outside the Wikipedia site and apps: for example, in Google searches.

文章描述总结了一篇文章，以帮助读者一目了然地理解这一主题. 这些在维基媒体社群中称为：维基数据描述（Wikidata descriptions）.

创建条目描述的提示
理想情况下，条目描述应该在一行上，并且长度在两到十二个字之间. 除非第一个单词是专有名词，否则它们不会大写，并且通常不会以初始文章（a，an，the）开头. 例如：


 * 李安納度·達文西的绘画（关于蒙娜丽莎的文章的条目描述）
 * 地球上最高的山峰（有关珠穆朗玛峰的文章的条目描述）

编写条目描述的其他提示：

(e.g., ‘current Prime Minister of…’) (e.g., ‘the best…’)
 * Avoid information that is likely to change
 * Avoid opinionated, biased or promotional wording
 * Avoid controversial claims

More information is available on the Wikidata descriptions help page.

More on article descriptions
条目描述在维基数据上存储和维护，维基媒体基金会提供免费、协作及多语言的辅助数据库，支持维基百科和其他维基媒体计划项目.


 * 关于维基数据
 * 维基数据帮助页面有关文章说明
 * 用英文撰写文章描述的指南

什么是图片说明？

 * 对图片简短的多语言描述
 * 长度限制为250个字符，不能包含标记

图像说明有什么用？
图片说明用于描述图片，帮助读者理解图片的含义和上下文. 如果有人无法查看图片，例如网速慢或使用屏幕阅读器时，图片说明也可以作为图片的替代信息.

创建图像说明的提示
图片说明应该是对图片展示的内容的简短描述. 它们的长度一般在四到十二个单词（英文）之间. 图片说明中也可以有图片的艺术家或创作者的信息.

创建图片说明时，您必须要保证说明的中立性，避免做出“美丽”“好”“丑”之类的主观判断.

The difference between the image description and the image caption is that the description can have a lot of information about the file. If the image is a scan it can have details about the original photograph or artwork. It can have links. The image caption should ignore all this and just describe what it shows, not the information about the file.

一个好的图片说明应该：


 * Aim to briefly describe the contents of an image
 * Describe the image such that those with vision or other impairments can imagine what it looks like
 * Contain some keywords that people are likely to use to search for an image (so a picture of a cat should include the word "cat" somewhere in the caption)

In many cases, the caption will be similar or identical to the description (or even the file title!). For example, File:Fire station Hallstatt - October 2017 - 02.jpg has the English caption "Hallstatt fire station in October 2017".

More on image captions
Image captions are stored and maintained on Wikimedia Commons, a project of the Wikimedia Foundation which provides an online repository of free-use images, sounds, other media, and JSON files supporting Wikipedia and other projects.

图片标签

 * 参见： Commons:Depicts 

什么是图片标签？
"Image tags" is a shorthand phrase for what the Commons community calls Depicts statements.

图片标签有什么用？
By adding image tags, you will help make images easier to search for on Commons, the free license image repository that Wikipedia uses for images in its articles.

添加图片标签的提示
Please add tags conservatively. If there are multiple items clearly and deliberately depicted by the image, all should be added as separate tags, within reason. For example, should be tagged with "Bonnie and Clyde", "Bonnie Parker", and "Clyde Barrow". Identify the most important thing(s) in the image.

'''尽可能具体. ''' Search for the most relevant tags by tapping "+ Add tag". You may see a number of search results, but resist the urge to add a large number of semi-relevant tags. In the example shown below, the picture is of the Williamsburg Bridge, but that tag has not been added. Add it yourself by tapping "+ Add tag" and searching for "Williamsburg Bridge".

Where is the train algorithm task?
Due to the train image algorithm task serving as a MVP for the Android team to learn from and build the full image matching feature, the train image algorithm MVP has been sunset. With the help of Wikipedians like you, we have improved the algorithm and have enough learnings to proceed with the next phase of our work to build the full image matching feature. Our lessons from the experiment will be available on the Add an Image project page. You can watch participate in the next phase of this work at Growth team's project page.

What is the train algorithm task?
Wikipedia articles are written and edited by thousands of volunteers from around the world. Unfortunately, many articles lack images. The Train Algorithm task is a type of Suggested Edits task that will show logged-in Android users articles and images along with its associated information, so that users can determine if the image is a good illustration of the contents of the article displayed.

The images will be suggested to you using an algorithm. The algorithm will pull images from other sources and suggest a match with an article that does not have an image.

Unlike other Suggested Edits tasks, the Train Algorithm task will not save any edits to any Wiki projects and is a temporary task. The purpose of the Train Algorithm task is to gather data, improve our image matching algorithm, and inform our design for future releases of an image matching task on Android and Mobile Web.



Tips for training the image algorithm
To best determine if an image is the right match for an article you should review:
 * Image (zoom in to review more details)
 * Image file name
 * Image description
 * Image suggestion reason
 * Article content

Dialog options
In the task you can select Yes, No or Not Sure, to the question of if you would add the image to an article.


 * Yes indicates the image is a good illustration to help readers understand the topic of the Wikipedia article
 * No indicates the image would not help readers understand the topic of the Wikipedia article. Reasons the image would not help be a good fit for the article includes:
 * Not Relevant, which indicates the image depicts a topic that is not associated with the article that is being shown
 * Not enough Information, which indicates the metadata shared with the image does not provide enough details for you to confidently indicate the image would help readers better understand the article
 * Offensive, which indicates the image is inappropriate
 * Low Quality, which indicates you can not see the image well enough to confidently say it is a good illustration of the article being shown and help readers understand the topic
 * Don't know this subject, which indicates you do not feel you have the needed expertise to determine if the image would help readers understand the topic of the Wikipedia article
 * Cannot read the language, which indicates the words written in the image, or the metadata is in a language you do not understand, and are then unable to confidently say the image would help readers understand the topic of the Wikipedia article
 * Other, which indicates there is another reason the image is not a good match for the article
 * Not Sure indicates you are not certain whether or not the image would help readers understand the topic of the Wikipedia article. Reasons for not being sure could include:
 * Not enough Information, which indicates the metadata shared with the image does not provide enough details for you to confidently indicate the image would help readers better understand the article
 * Cannot see image, which indicates you can not see the image well enough to confidently say it is a good illustration of the article being shown and help readers understand the topic
 * Don't know this subject, which indicates you do not feel you have the needed expertise to determine if the image would help readers understand the topic of the Wikipedia article
 * Don't understand the task, which indicates you do not understand the Train Image Algorithm task
 * Cannot read the language, which indicates the words written in the image, or the metadata is in a language you do not understand, and are then unable to confidently say the image would help readers understand the topic of the Wikipedia article
 * Other, which indicates there is another reason you are not sure if the image would be a good match for the article

Daily goal
The 'Train image algorithm' task consists of a daily goal. To reach the daily goal, you need to evaluate 10 image suggestions. The goal resets to 0 on the next day. You can always evaluate more than 10 image suggestions per day, this should just serve as an indicator of your daily progress.

When will you roll out the full image matching task?
The Android and Growth teams will collaboratively evaluate the outcomes of this MVP, and determine next steps for a task that places images in articles in July 2021. During that time, the teams will update the MVP project page with our findings and share when we will offer the image matching task.

How can I follow the outcome of this MVP when it ends?
You can watch our Add an Image MVP project page for updates about this effort. We would also value your feedback on our talk page.