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Related Concept Videos

Estimation of the Physical Quantities01:05

Estimation of the Physical Quantities

On many occasions, physicists, other scientists, and engineers need to make estimates of a particular quantity. These are sometimes referred to as guesstimates, order-of-magnitude approximations, back-of-the-envelope calculations, or Fermi calculations. The physicist Enrico Fermi was famous for his ability to estimate various kinds of data with surprising precision. Estimating does not mean guessing a number or a formula at random. Instead, estimation means using prior experience and sound...
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Physiological Pharmacokinetic Models: Blood Flow-Limited Versus Diffusion-Limited Models

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Model Approaches for Pharmacokinetic Data: Compartment Models

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Carbon-dioxide Fixation01:28

Carbon-dioxide Fixation

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Related Experiment Video

Updated: May 13, 2026

Measurements of CO2 Fluxes at Non-Ideal Eddy Covariance Sites
09:05

Measurements of CO2 Fluxes at Non-Ideal Eddy Covariance Sites

Published on: June 24, 2019

Constrained Carbon Partitioning: A Self-Trained Physics-Informed Machine Learning Model Refines GPP Estimates From

Sadegh Ranjbar1, Ankur R Desai2, Sophie Hoffman1

  • 1Department of Biological Systems Engineering, University of Wisconsin - Madison, Madison, Wisconsin, USA.

Global Change Biology
|May 12, 2026
PubMed
Summary

A new knowledge-guided machine learning (KGML) framework accurately partitions net ecosystem exchange (NEE) into gross primary production (GPP) and ecosystem respiration (RECO). This method provides more accurate carbon flux estimates across diverse ecosystems.

Keywords:
Eddy covarianceKok effectNEONcarbon partitioningecosystem respirationgross primary productivityknowledge‐guided machine learningstomatal physiology

Related Experiment Videos

Last Updated: May 13, 2026

Measurements of CO2 Fluxes at Non-Ideal Eddy Covariance Sites
09:05

Measurements of CO2 Fluxes at Non-Ideal Eddy Covariance Sites

Published on: June 24, 2019

Area of Science:

  • Ecology
  • Biogeochemistry
  • Machine Learning

Background:

  • Gross primary productivity (GPP) is a critical component of the global carbon budget but is not directly measurable.
  • Accurate partitioning of net ecosystem exchange (NEE) into GPP and ecosystem respiration (RECO) is essential for understanding carbon cycling.

Purpose of the Study:

  • To develop and validate a knowledge-guided machine learning (KGML) framework for partitioning NEE into GPP and RECO.
  • To assess the performance of KGML against conventional methods and explore its implications for carbon flux estimation.

Main Methods:

  • Utilized eddy covariance data from 36 U.S. National Ecological Observatory Network (NEON) towers.
  • Developed a KGML framework incorporating physical constraints and ecophysiological expectations (e.g., stomatal response to vapor pressure deficit).
  • Partitioned water vapor fluxes and CO2 flux source areas to inform the model.

Main Results:

  • KGML demonstrated strong physical consistency (NEE R² = 0.99) and captured ecophysiological relationships (GPP-T R² = 0.58).
  • KGML inferred lower GPP and RECO estimates compared to conventional methods, particularly at low light levels.
  • Flux differences varied across plant functional types (PFTs), with forests showing the largest negative GPP deviations.

Conclusions:

  • The KGML framework offers a robust approach for partitioning NEE into GPP and RECO, improving carbon flux estimation accuracy.
  • Lower GPP estimates from KGML align with findings that incorporate respiration limitations (e.g., Kok effect).
  • This approach enhances our understanding of ecosystem and global carbon cycles by leveraging combined physical and biological knowledge.