Development and machine learning-based calibration of low-cost multiparametric stations for the measurement of CO2 and CH4 in air Article Swipe

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R. Biagi , Marco Ferrari , Stefanía Venturi , Michael Sacco , Giordano Montegrossi , Franco Tassi ·

YOU? · · 2024 · Open Access · · DOI: https://doi.org/10.1016/j.heliyon.2024.e29772

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Calibration

Environmental Science

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Reliability (semiconductor) Calibration Environmental science Computer science Field (mathematics) Remote sensing Process engineering Engineering Statistics Mathematics Geology Pure mathematics Power (physics) Quantum mechanics Physics

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Type: article
Language: en
Landing Page: https://doi.org/10.1016/j.heliyon.2024.e29772
PDF: http://www.cell.com/article/S2405844024058031/pdf
OA Status: gold
Cited By: 9
References: 54
Related Works: 10
OpenAlex ID: https://openalex.org/W4395080551

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OpenAlex ID: https://openalex.org/W4395080551

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DOI: https://doi.org/10.1016/j.heliyon.2024.e29772

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Title: Development and machine learning-based calibration of low-cost multiparametric stations for the measurement of CO2 and CH4 in air

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Type: article

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Language: en

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Publication year: 2024

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Publication date: 2024-04-23

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Authors: R. Biagi, Marco Ferrari, Stefanía Venturi, Michael Sacco, Giordano Montegrossi, Franco Tassi

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Landing page: https://doi.org/10.1016/j.heliyon.2024.e29772

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PDF URL: https://www.cell.com/article/S2405844024058031/pdf

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Concepts: Reliability (semiconductor), Calibration, Environmental science, Computer science, Field (mathematics), Remote sensing, Process engineering, Engineering, Statistics, Mathematics, Geology, Pure mathematics, Power (physics), Quantum mechanics, Physics

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Cited by: 9

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