Cute Baby Elephant
I hope you’ve been having fun with the new Season 5 images. I have. It’s been about a week since we went live with Season 5, and we’re making good progress. It took under two weeks to go through the first three seasons in December. (We had some media attention then and lots of people checking out the site.) It took about three weeks to finish Season 4 in January. According to my super science-y image copy-and-paste method, it may take us about two months to do Season 5:
And that’s fine. But I was curious about who’s working on Season 5. The Talk discussion boards are particularly quiet, with almost no newbie questions. So is everyone working on Season 5 a returnee? Or do we have new folks on board?
I looked at the user data from a data dump done on Sunday. So it includes the first 5 or so days of Season 5. In total, there are 2,000 volunteers who had contributed to 280,000 classifications by Sunday! I was actually quite amazed to see that 6% of the classifications are being done by folks not logged in. Is that because they’re new people trying out the site — or because there are some folks who like to classify without logging in? I can’t tell.
But I can compare Season 5 to Season 4. We had 8,300 logged-in volunteers working on Season 4. Of all the classifications, 9% were done by not-logged-in folks. That suggests we have fewer newcomers so far for Season 5. But then we get to an intriguing statistic: of those 2,000 volunteers working on Season 5 in its first five days, 33% of them did not work on Season 4 at all! And those 33% apparently new folks have contributed 50% of the (logged-in) classifications!
So what’s going on? Maybe we’re getting these new volunteers from other Zooniverse projects that have launched since January. Maybe they’re finding us in other ways. (Have you seen that the site can be displayed in Finnish in addition to Polish now?) But in any case, welcome everyone and I hope you spot your favorite animal.
Me, I found this super cute baby elephant just the other day:
Some Results from Season 4
I was asked in the comments to last week’s blog post if I could provide some feedback about the results of Season 4. If you felt like you were seeing a lot of “nothing here” images, you’re right: of the 158,098 unique capture events we showed you, 70% were classified as having no animals in them. That left 47,320 with animals in them to classify, and the vast majority of these (94%) contained just one species. Here’s the breakdown of what was in all those images:
Maybe it won’t surprise you that Season 4 covered 2012’s wet season, when over a million wildebeest, zebra, and Thomson’s gazelle migrate through our study area. I find it interesting that hartebeest are also pretty numerous, but I wonder if it’s because of that one hartebeest that stood in front of the camera for hours on end.
This pie chart is based on the number of what we call “capture events,” which is the set of 1 or 3 pictures you see every time you make a classification. Once a camera has taken a set of pictures, we delay it from triggering again for about a minute. That way we don’t fill up the camera’s memory card with too many repeats of the same animals before we have a chance to replace them. But a minute isn’t a very long time for an animal that has decided to camp out in front of a camera, and so we frequently get sequences of many capture events that are all of the same animal. One of the things we’ll have to do in turning your classifications into valid research results is to figure out how to find these sequences in the data automatically.
Here’s a sequence of an elephant family hanging out around our camera for the night about a year ago. (Hat tip to dms246 who put together a collection of most of these images to answer the concerned question of some classifiers who saw just one image out of the whole sequence: is that elephant dead or just sleeping?)
1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31
If you’re interested in how I made the above pie chart, keep reading. But we’re going to get technical here, so if algorithms don’t interest you, feel free to stop.