首页|New Findings from University of Toronto in Machine Learning Provides New Insight s (Global Datasets of Hourly Carbon and Water Fluxes Simulated Using a Satellite -based Process Model With Dynamic Parameterizations)
New Findings from University of Toronto in Machine Learning Provides New Insight s (Global Datasets of Hourly Carbon and Water Fluxes Simulated Using a Satellite -based Process Model With Dynamic Parameterizations)
扫码查看
点击上方二维码区域，可以放大扫码查看
原文链接
NETL
NSTL
By a News Reporter-Staff News Editor at Robotics & Machine Learning DailyNews Daily News – Investigators discuss new findings in Machine Learning. According to news reportingoriginating in Toronto, Canada, by NewsRx journalists, research stated, “Diagnostic terrestrial biospheremodels ( TBMs) forced by remote sensing observations have been a principal tool for provi ding benchmarkson global gross primary productivity (GPP) and evapotranspiratio n (ET). However, these models oftenestimate GPP and ET at coarse daily or month ly steps, hindering analysis of ecosystem dynamics at thediurnal (hourly) scale s, and prescribe some essential parameters (i.e., the Ball-Berry slope ( m ) and themaximum carboxylation rate at 25 degrees;C ( V-25 (cmax) )) as constant, in ducing uncertainties in theestimates of GPP and ET.”
TorontoCanadaNorth and Central Ameri caCyborgsEmerging TechnologiesMachine LearningRemote SensingUniversity of Toronto
NETL
NSTL
