Preprints
Filtering by Subject: Animal Sciences
Resilient foods for preventing global famine: a review of food supply interventions for global catastrophic food shocks including nuclear winter and infrastructure collapse
Published: 2024-09-12
Subjects: Agricultural Science, Agriculture, Animal Sciences, Aquaculture and Fisheries Life Sciences, Chemical Engineering, Engineering, Food Science, Life Sciences, Plant Sciences, Risk Analysis
Global catastrophic threats to the food system upon which human society depends are numerous. A nuclear war or volcanic eruption could collapse agricultural yields by inhibiting crop growth. Nuclear electromagnetic pulses or extreme pandemics could disrupt industry and mass-scale food supply by unprecedented levels. Global food storage is limited. What can be done? This article presents the state [...]
Differences in running velocity and boldness between male and female Atlantic sand fiddler crab (Leptuca pugilator)
Published: 2022-02-19
Subjects: Animal Sciences, Ecology and Evolutionary Biology, Marine Biology
Atlantic sand fiddler crabs (Leptuca pugilator) exhibit an extreme case of sexual dimorphism with the male crabs wielding an enlarged dominate claw that can account up to 40% of an individual’s total body mass. The salt pans found in marine marshes are commonly colonized by fiddler crabs and have limited coverage from avian predators, making the ability to quickly run back their burrows, an [...]
Fish species classification in underwater video monitoring using Convolutional Neural Networks
Published: 2018-05-15
Subjects: Animal Sciences, Aquaculture and Fisheries Life Sciences, Computer Sciences, Environmental Monitoring, Environmental Sciences, Life Sciences, Other Life Sciences, Physical Sciences and Mathematics, Software Engineering
This report presents a case study for automatic fish species classification in underwater video monitoring of fish passes. Although the presented approach is based on the FishCam monitoring system, it can be used with any video-based monitoring system. The presented classification scheme in this study, is based on Convolutional Neural Networks that do not require the calculation of any [...]