Robotics & Machine Learning Daily News2024,Issue(Apr.11) :86-87.

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)

Robotics & Machine Learning Daily News2024,Issue(Apr.11) :86-87.

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)

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Abstract

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.”

Key words

Toronto/Canada/North and Central Ameri ca/Cyborgs/Emerging Technologies/Machine Learning/Remote Sensing/University of Toronto

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出版年

2024
Robotics & Machine Learning Daily News

Robotics & Machine Learning Daily News

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