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Record W4414950409 · doi:10.1029/2025jg009209

Nitrogen Cycling in Earth System Models: From Constraining Carbon Budgets to Projecting Pollution for Planetary Stewardship

2025· article· en· W4414950409 on OpenAlexafffund
Sian Kou‐Giesbrecht

Bibliographic record

VenueJournal of Geophysical Research Biogeosciences · 2025
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicAtmospheric chemistry and aerosols
Canadian institutionsSimon Fraser University
FundersNatural Sciences and Engineering Research Council of CanadaLiber Ero Foundation
KeywordsHydrosphereNitrogen cycleEarth system scienceBiosphereClimate changeCarbon cycleGreenhouse gasReactive nitrogenBiogeochemistryCarbon sequestration

Abstract

fetched live from OpenAlex

Abstract Terrestrial nitrogen cycling plays a vital role in the Earth system, influencing climate change and a myriad of dimensions of human well‐being. Earth system models (ESMs) are used to project climate change and increasingly include a representation of terrestrial nitrogen cycling. In this review, we highlight how ESMs primarily focus on nitrogen limitation of primary productivity and carbon sequestration but generally neglect nitrogen losses from the terrestrial biosphere to the atmosphere and hydrosphere which are strongly influenced by human activities. These include key flows of nitrogen such as nitrogen gas emissions from soils and wildfires as well as its transport through the land to ocean aquatic continuum. ESMs with fully interactive nitrogen cycling could both improve climate change projections and be used to project nitrogen pollution and its impacts to inform planetary stewardship over the 21st century.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.009
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.024
Threshold uncertainty score0.048

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.009
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0020.002
Open science0.0010.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0020.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.060
GPT teacher head0.318
Teacher spread0.258 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations2
Published2025
Admission routes2
Has abstractyes

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