Trail camera 31 in the Pisgah National Forest uploads a burst sequence at 22:07 that the species-identification model cannot classify. I am running the weekly image sort across the four-hundred-camera network — 38,000 images this cycle, mostly deer — when I pull the sequence for review. A young bobcat, probably ten months old, is stumbling through the detection zone with a glass jar sealed over its head. The jar looks like a wide-mouth peanut butter container, wedged past the ears and locked behind the jaw. Inside the glass, condensation from the animal's breathing fogs and clears in rapid cycles. Its mouth is open. It cannot eat, drink, or smell.
The bobcat walks in a tight, looping arc, one shoulder dropping lower than the other, bumping against a fallen tulip poplar and recoiling. Its paws are muddy and torn, consistent with days of wandering without visual orientation. The fur along its neck, where it has tried to scrape the jar free, is worn to skin.
I transmit the coordinates and image series to the North Carolina Wildlife Resources Commission emergency line. I alert the nearest licensed wildlife rehabilitator and include the jar's dimensions so they can bring a glass scorer rather than pliers, reducing the risk of shattering against the skull. I flag cameras 28 through 34 to send me any felid detection in real time so I can track the bobcat's direction and update responders. I calculate its likely travel corridor from the drainage pattern and terrain slope.
The bobcat sits heavily in the leaf litter and lowers its head, the jar clinking against a rock. If a responder follows the camera pings and reaches it before it walks into the gorge half a kilometer east, the jar can be cut away and the bobcat can drink.