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High-altitude conditions on the Tibetan Plateau are often depicted as an inhospitable environment for conventional farming, yet evidence shows that communities in western Tibet grew ecologically hardy crops such as 6-row barley (Hordeum vulgare) by at least the 1^stmillennium BCE, at locations above 4,000 meters above sea level (masl). However, little is known about the specific cultivation strategies and culinary traditions that these agropastoral communities developed. Stable carbon and nitrogen isotope compositions of grains inform growing conditions and provide much needed insight into the cultivation strategies in such a unique environment. We use δ¹³C and δ¹⁵N values of archaeologically recovered barley remains to investigate past watering and soil-management strategies. Our results infer high labor investment in manuring and watering in barley farming. This suggests an intensive cultivation system in Western Tibet, 1,000 BCE −1,000 CE, despite the high-altitude pastoral landscape. 

Despite significant advances in machine learning (ML) applications within science, there is a notable gap in its integration into K-12 education to enhance data literacy and scientific inquiry (SI) skills. To address this gap, we enable K-12 teachers with limited technical expertise to apply ML for pattern discovery and explore how ML can empower educators in teaching SI. We design a web-based tool, ML4SI, for teachers to create ML-supported SI learning activities. This tool can also facilitate collecting data about the interaction between ML techniques and SI learning. A pilot study with three K-12 teachers provides insights to prepare the next generation for the era of big data through ML-supported SI learning.