Приклади вживання The galaxy zoo Англійська мовою та їх переклад на Українською
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The Galaxy Zoo.
This was the idea that led to the Galaxy Zoo project.
For the Galaxy Zoo team, this first project was just the beginning.
This is another groundbreaking discovery made by the Galaxy Zoo project.
At the Galaxy Zoo website, volunteers would undergo a few minutes of training;
They were found by citizen scientists taking part in the Galaxy Zoo project.
The Galaxy Zoo project used more than 100,000 volunteers to categorize more than 900,000 images.
Discovered in 2007 by Hanny van Arkel,a Dutch schoolteacher participating in the Galaxy Zoo project.
Then, for a subset of the images, the Galaxy Zoo labels are used to train a machine learning model.
In the Galaxy Zoo family of projects, extremely active and important contributors are sometimes invited to be coauthors on papers.
For example, the Galaxy Zoo community has discovered and a new class of astronomical object that they called“Green Peas.”.
With the help of Amateur astronomers involved in the project called the Galaxy Zoo, he was marked as a system of colliding galaxies. .
(2010) used the Galaxy Zoo classifications to train a machine learning model to do galaxy classification.
Because no readily available software existed for the job, he decided to crowdsource it-and so the Galaxy Zoo citizen science project was born.
For example, the Galaxy Zoo community has discovered a new class of astronomical object that they called“Green Peas.”.
Therefore, Manda Banerji- working with Schawinski, Lintott,and other members of the Galaxy Zoo team(2010)- started teaching computers to classify galaxies. .
For example, the Galaxy Zoo team ran an open call and found a new approach that outperformed the one developed in Banerji et al.
Because very similar challenges arise in most human computation projects,it is helpful to briefly review the three steps that the Galaxy Zoo researchers used to produce their consensus classifications.
For example, the Galaxy Zoo team ran an open call and found a new approach that outperformed the one developed in Banerji et al.(2010);
Sloan data is also behind the Google Sky service, which allows users to scan the heavens in the same way as scanning their local streets, and the Galaxy Zoo project, which has allowed astronomy enthusiasts to characterise galaxies from their own computers.
Very early in the Galaxy Zoo project, a few people had noticed unusual green objects, but interest in them crystallized when Hanny Von Arkel, a Dutch school teacher, started a thread in the Galaxy Zoo discussion forum with the catchy title:“Give Peas a Chance.”The thread, which began August 12, 2007, started with jokes:“Are you collecting them for dinner?”,“Peas stop”, and so on?
Thus, after a three-step process- cleaning, debiasing, and weighting- the Galaxy Zoo research team had converted 40 million volunteer classifications into a set of consensus morphological classifications.
Very early in the Galaxy Zoo project, a few people had noticed unusual green objects, but interest in them crystallized when Hanny van Arkel, a Dutch school teacher, started a thread in the Galaxy Zoo discussion forum with the catchy title:“Give Peas a Chance.”The thread, which began August 12, 2007, started with jokes:“Are you collecting them for dinner?,”“Peas stop,” and so on.
Finally, in chapter 5, we saw how Kevin Schawinski,Chris Lintott, and the Galaxy Zoo team created a mass collaboration that motivated more than 100,000 people to participate in an astronomical(in both senses of the word) image labeling task(Lintott et al. 2011).
They were first spotted in 2007 by participants in the astronomical crowd-sourcing project Galaxy Zoo.
Not only is the micro-task in Galaxy Zoo quite general,the structure of project is general as well.
Building on Galaxy Zoo, the researchers completed Galaxy Zoo 2 which collected more than 60 million more complex morphological classifications from volunteers(Masters et al. 2011).