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Lead author Karen Chin of the University of Colorado Boulder
Courtesy the University of Colorado Boulder

Fossilized Poop Shows Some Herbivorous Dinosaurs Loved a Good Crab Dinner

Original image
Lead author Karen Chin of the University of Colorado Boulder
Courtesy the University of Colorado Boulder

Scientists can learn a lot about the prehistoric world through very, very old poop. Just recently, researchers from the University of Colorado-Boulder and Kent State University studying fossilized dinosaur poop discovered that some herbivores weren't as picky about their diets as we thought. Though they mostly ate plants, large dinosaurs living in Utah 75 million years ago also seem to have eaten prehistoric crustaceans, as Nature News reports.

The new study, published in Scientific Reports, finds that large dinosaurs of the Late Cretaceous period seem to have eaten crabs, along with rotting wood, based on the content of their coprolites (the more scientific term for prehistoric No. 2). The fossilized remains of dinos' bathroom activities were found in the Kaiparowits rock formation in Utah's Grand Staircase-Escalante National Monument, a known hotspot for pristine Late Cretaceous fossils.

"The large size and woody contents" of the poop suggest that they were created by dinosaurs that were well-equipped to process fiber in their diets, as the study puts it, leading the researchers to suggest that the poop came from big herbivores like hadrosaurs, whose remains have been found in the area before.

Close up scientific images of evidence of crustaceans in fossilized poop.
Chin et al., Scientific Reports (2017)

While scientists previously thought that plant-eating dinosaurs like hadrosaurs only ate vegetation, these findings suggest otherwise. "The diet represented by the Kaiparowits coprolites would have provided a woody stew of plant, fungal, and invertebrate tissues," the researchers write, including crabs (Yum). These crustaceans would have provided a big source of calcium for the dinosaurs, and the other invertebrates that no doubt lived in the rotting logs would have provided a good source of protein.

But they probably didn't eat the rotting wood all year, instead munching on dead trees seasonally or during times when other food sources weren’t available. Another hypothesis is that these "ancient fecal producers," as the researchers call them, might have eaten the rotting wood, with its calcium-rich crustaceans and protein-laden invertebrates, during egg production, similar to the feeding patterns of modern birds during breeding season.

Regardless of the reason, these findings could change how we think about what big dinosaurs ate.

[h/t Nature News]

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Lead author Karen Chin of the University of Colorado Boulder
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Google's AI Can Make Its Own AI Now
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iStock

Artificial intelligence is advanced enough to do some pretty complicated things: read lips, mimic sounds, analyze photographs of food, and even design beer. Unfortunately, even people who have plenty of coding knowledge might not know how to create the kind of algorithm that can perform these tasks. Google wants to bring the ability to harness artificial intelligence to more people, though, and according to WIRED, it's doing that by teaching machine-learning software to make more machine-learning software.

The project is called AutoML, and it's designed to come up with better machine-learning software than humans can. As algorithms become more important in scientific research, healthcare, and other fields outside the direct scope of robotics and math, the number of people who could benefit from using AI has outstripped the number of people who actually know how to set up a useful machine-learning program. Though computers can do a lot, according to Google, human experts are still needed to do things like preprocess the data, set parameters, and analyze the results. These are tasks that even developers may not have experience in.

The idea behind AutoML is that people who aren't hyper-specialists in the machine-learning field will be able to use AutoML to create their own machine-learning algorithms, without having to do as much legwork. It can also limit the amount of menial labor developers have to do, since the software can do the work of training the resulting neural networks, which often involves a lot of trial and error, as WIRED writes.

Aside from giving robots the ability to turn around and make new robots—somewhere, a novelist is plotting out a dystopian sci-fi story around that idea—it could make machine learning more accessible for people who don't work at Google, too. Companies and academic researchers are already trying to deploy AI to calculate calories based on food photos, find the best way to teach kids, and identify health risks in medical patients. Making it easier to create sophisticated machine-learning programs could lead to even more uses.

[h/t WIRED]

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Lead author Karen Chin of the University of Colorado Boulder
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European Space Agency Releases First High-Res Land Cover Map of Africa
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Land Cover CCI, ESA

This isn’t just any image of Africa. It represents the first of its kind: a high-resolution map of the different types of land cover that are found on the continent, released by The European Space Agency, as Travel + Leisure reports.

Land cover maps depict the different physical materials that cover the Earth, whether that material is vegetation, wetlands, concrete, or sand. They can be used to track the growth of cities, assess flooding, keep tabs on environmental issues like deforestation or desertification, and more.

The newly released land cover map of Africa shows the continent at an extremely detailed resolution. Each pixel represents just 65.6 feet (20 meters) on the ground. It’s designed to help researchers model the extent of climate change across Africa, study biodiversity and natural resources, and see how land use is changing, among other applications.

Developed as part of the Climate Change Initiative (CCI) Land Cover project, the space agency gathered a full year’s worth of data from its Sentinel-2A satellite to create the map. In total, the image is made from 90 terabytes of data—180,000 images—taken between December 2015 and December 2016.

The map is so large and detailed that the space agency created its own online viewer for it. You can dive further into the image here.

And keep watch: A better map might be close at hand. In March, the ESA launched the Sentinal-2B satellite, which it says will make a global map at a 32.8 feet-per-pixel (10 meters) resolution possible.

[h/t Travel + Leisure]

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