AI enables a Who's Who of brown bears in Alaska Being able to distinguish individual animals - including their unique history, movement patterns and habits - can help scientists better understand how their species function, and therefore better manage habitats and study population dynamics. Today, most computer vision systems for tracking animals are effective on species with patterns and markings, such as zebras, leopards and giraffes. The task is much more complicated for unmarked species where individual differences are harder to spot. Distinguishing a particular brown bear from its peers in a non-invasive way requires an incredible eye for detail and years of viewing the same bears over time. What's more, these bears emerge from hibernation in the spring with shaggy fur and having lost quite a bit of weight and then substantially increase their body weight feasting on salmon, as well as fully shedding their winter coat - that's enough to throw off experts as well as AI algorithms.

We introduce a natural and mild restriction on the meta-level combination functions of the local trials.

RCM. Finally, we illustrate the power of this conciliatory perspective by pinpointing

High-stakes prediction tasks (e.g., patient diagnosis) are often handled by trained

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Although freely accessible, it is provided in individual files for each year, and is difficult to combine in order to analyze spatial or temporal trends.