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

Sadegh Ranjbar, Ankur R Desai, Sophie Hoffman, Einara Zahn, Elie Bou-Zeid, Paul C Stoy

Abstract read

In one paragraph

Article in Global change biology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it

0cells of the map it votes in

1citing papers in PubMed

–field-weighted citation impact

1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

Constrained Carbon Partitioning: A Self-Trained Physics-Informed Machine Learning Model Refines GPP Estimates From Eddy Covariance Measurements.Global change biology · 2026
Article

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Sadegh RanjbarDepartment of Biological Systems Engineering, University of Wisconsin - Madison, Madison, Wisconsin, USA.

Ankur R DesaiDepartment of Atmospheric and Oceanic Sciences, University of Wisconsin - Madison, Madison, Wisconsin, USA.

Sophie HoffmanDepartment of Biological Systems Engineering, University of Wisconsin - Madison, Madison, Wisconsin, USA.

Einara ZahnDepartment of Civil and Environmental Engineering, Princeton University, Princeton, New Jersey, USA.

Elie Bou-ZeidDepartment of Civil and Environmental Engineering, Princeton University, Princeton, New Jersey, USA.

Paul C StoyDepartment of Biological Systems Engineering, University of Wisconsin - Madison, Madison, Wisconsin, USA.

Funding

U.S. National Science Foundation 2422397

6 · The paper itself

Abstract

Gross primary productivity (GPP) is the largest term in the global carbon budget but cannot be directly observed. We present a knowledge-guided machine learning (KGML) framework that partitions eddy covariance-measured net ecosystem exchange (NEE) into gross primary production (GPP) and ecosystem respiration (RECO) with partitioned water vapor fluxes and CO

Indexed as

Carbon CycleEcosystemMachine LearningCarbon DioxideCarbon Dioxidecarbon partitioningecosystem respirationEddy covariancegross primary productivityknowledge‐guided machine learningKok effectNEONstomatal physiology

Identifiers

PMID42116715

DOI10.1111/gcb.70886

PMCPMC13161833

What OpenQuestion holds

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LicenceCC BY

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.