2020 Climate and Life Interns
Due to COVID-19, this year's program was virtual, with students working individually and in small groups on research projects designed and led by our scientists. By the end of their internship, students understand what a career in science involves. They also gain critical science skills, including how to read research papers, perform measurements, collect and present data, and report on their research.
We were pleased to host the following seven students in 2020.
Project: Forecasting Food Supplies and Nutritional Content | Mentor: Benjamin Bostick
Rice is the most consumed food on the plant, representing approximately 20 percent of all calories consumed by humanity. Climate and climate variability play a central and well-understood role in regulating rice paddy wetness and yields, but it also has the potential to affect the nutritional content of the rice, and potentially the concentration of contaminants like arsenic. Arsenic is a major human health hazard in groundwater, soil, and food in both the developing and developed world, which is often abundant in rice. Exposure to this arsenic in rice potentially is a major human health risk to people who depend on that rice as a staple. This project will involve placing existing data about rice nutritional content and arsenic levels in context, and to study their connection to the microscopic hydrological and climatological variation in growing conditions and in the biology and physiology of the rice plant and indigenous soil bacteria.
Read about the results of this research: Interns Find Links Between Climate and Arsenic Levels in Rice
Harrison N. Gerson
Project: Classifying Fracture Patterns on Antarctica | Mentor: Jonny Kingslake
Ice fractures (crevasses) facilitate ice-shelf collapse, impact ice flow, and are a major obstacle to polar fieldwork. Yet, no ice-sheet-wide crevasse mapping has been undertaken, mainly because high enough resolution imagery was unavailable and no practical method for detecting crevasses continent-wide has been adopted by glaciologists. Now, with new high-resolution satellite imagery (Digital Globe, DG, imagery) and recent developments in machine learning for image classification in other fields, continent-wide crevasse mapping is a possibility. We have used a deep convolutional neural network (DCNN) to recognize fracture patterns in low-resolution satellite imagery (125-m) across the Antarctic’s ice shelves (Lai et al., under review). However, many fine fractures are only visible in higher-resolution DG imagery and the DCNN’s performance is expected to improve with image resolution. The goal of this project is to teach students how to classify fracture patterns on Antarctica using DG imagery. The trained deep learning model will be applied to map crevasses across Antarctica. The implementation of the DCNN on DG imagery will be undertaken in Pangeo, laying the groundwork for automatic mapping of other small-scale surface features in future work.
Student: Russel Arbore