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Record W3032563911 · doi:10.1016/j.cosust.2020.03.008

Soil N intensity as a measure to estimate annual N2O and NO fluxes from natural and managed ecosystems

2020· article· en· W3032563911 on OpenAlexaff
Zhisheng Yao, David E. Pelster, Chunyan Liu, Xunhua Zheng, Klaus Butterbach‐Bahl

Bibliographic record

VenueCurrent Opinion in Environmental Sustainability · 2020
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicSoil Carbon and Nitrogen Dynamics
Canadian institutionsAgriculture and Agri-Food Canada
FundersNational Natural Science Foundation of ChinaNational Key Research and Development Program of ChinaDeutsche ForschungsgemeinschaftKarlsruhe Institute of Technology
KeywordsEnvironmental scienceEcosystemTerrestrial ecosystemNitrous oxideGreenhouse gasAtmospheric sciencesIntensity (physics)AgricultureSoil scienceHydrology (agriculture)EcologyGeology

Abstract

fetched live from OpenAlex

As natural and managed terrestrial ecosystems are major sources of the potent greenhouse gas nitrous oxide (N2O) and of the atmospheric pollutant nitric oxide (NO), predicting the source strengths of these ecosystems is central to understanding and sustainably managing the N-oxides fluxes. Here we reviewed 82 high temporal resolution datasets on N2O and 57 on NO fluxes collected from multi-site and multi-year field measurements, including grasslands, forests, and agricultural crops, to assess whether soil N intensity, that is, the time-weighted sum of soil NH4+ and/or NO3− concentrations, can be used to estimate annual N-oxides emissions. We show that soil N intensity alone can accurately predict annual N2O and NO emissions, and that the N2O emission strength is exponentially related to the soil inorganic N load. Thus, measuring soil inorganic N loads should improve current estimates of N-oxide emissions from global terrestrial ecosystems, and open possibilities for monitoring N2O mitigation efforts.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.065
Threshold uncertainty score0.419

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.013
GPT teacher head0.247
Teacher spread0.234 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations35
Published2020
Admission routes1
Has abstractyes

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