摘要
由一名新闻记者兼机器人与机器学习每日新闻编辑-研究人员详细介绍了机器学习的新数据。根据NewsRx Correspo Ndents从俄亥俄州哥伦布市发回的新闻报道，研究表明：“气孔导度（GS）是控制植被和地表环境之间水分、碳和能量交换的关键叶级功能。传统上，半经验模型被用于模拟LGS，但随着生态系统在生长季节经历表型变化，这些模型需要重新参数化。”本研究的资助者包括美国国家科学基金会(NSF)、美国国家航空航天管理局(NASA)、俄亥俄州立大学食品、农业和环境科学学院。
Abstract
By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Researchers detail new data in Machine Learning. According to news originating from Columbus, Ohio, by NewsRx correspo ndents, research stated, “Stomatal conductance (gs) is a key leaflevel function controlling water, carbon, and energy exchange between vegetation and the surro unding environment. Conventionally, semi-empirical models have been used to mode l gs, but these models require re-parameterization as ecosystems undergo phenolo gical changes over the growing season.” Funders for this research include National Science Foundation (NSF), National Ae ronautics & Space Administration (NASA), College of Food, Agricult ural and Environmental Sciences at Ohio State University.
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