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Record W3088594318 · doi:10.1016/j.cosust.2020.07.005

Estimating global terrestrial denitrification from measured N2O:(N2O + N2) product ratios

2020· article· en· W3088594318 on OpenAlexaff
Clemens Scheer, Kathrin Fuchs, David E. Pelster

Bibliographic record

VenueCurrent Opinion in Environmental Sustainability · 2020
Typearticle
Languageen
FieldEnvironmental Science
TopicSoil and Water Nutrient Dynamics
Canadian institutionsAgriculture and Agri-Food Canada
FundersBundesministerium für Bildung und ForschungDeutscher Akademischer Austauschdienst
KeywordsDenitrificationEnvironmental scienceReactive nitrogenTerrestrial ecosystemNitrogenAtmosphere (unit)EcosystemNitrous oxideAtmospheric sciencesEnvironmental chemistryEcologyChemistryMeteorologyGeographyGeologyBiology

Abstract

fetched live from OpenAlex

The use of nitrogen (N) fertilizers and cultivation of N-fixing crops has grown exponentially over the last century, with severe environmental consequences. Most of the anthropogenic reactive nitrogen will ultimately be returned by denitrification to the atmosphere as inert N2, but the magnitude of denitrification and the ratio of N2O to (N2O + N2) emitted (RN2O) is unknown for the vast majority of terrestrial ecosystems. This paper provides estimates of terrestrial denitrification and RN2O by reviewing existing literature and compiling a N budget for the global land surface. We estimate that terrestrial denitrification has doubled from 80 Tg-N year−1 in pre-industrial times to 160 Tg-N year−1 in 2005 with a mean RN2O of approximately 0.08. We conclude that upscaling of RN2O can provide spatial estimates of terrestrial denitrification when data from acetylene inhibition methods are excluded. Recent advances in methodologies to measure N2 emissions and RN2O under field conditions could open the way for more effective management of terrestrial N flows.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.061
Threshold uncertainty score1.000

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 head0.029
GPT teacher head0.280
Teacher spread0.250 · 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.

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

Citations112
Published2020
Admission routes1
Has abstractyes

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