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Record W2805351355 · doi:10.1002/2017gl075832

Dry Deposition of Reactive Nitrogen From Satellite Observations of Ammonia and Nitrogen Dioxide Over North America

2017· article· en· W2805351355 on OpenAlexafffundabout
Shailesh Kumar Kharol, Mark W. Shephard, C. A. McLinden, Leiming Zhang, Christopher E. Sioris, Jason O’Brien, Robert Vet, Karen Cady‐Pereira, E. W. Hare, Jacob Siemons, N. A. Krotkov

Bibliographic record

VenueGeophysical Research Letters · 2017
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicAtmospheric chemistry and aerosols
Canadian institutionsUniversity of WaterlooEnvironment and Climate Change Canada
FundersNatural Sciences and Engineering Research Council of CanadaNational Oceanic and Atmospheric AdministrationEnvironment and Climate Change CanadaUniversity of Wisconsin-MadisonNational Aeronautics and Space Administration
KeywordsNitrogenReactive nitrogenDeposition (geology)Flux (metallurgy)EcosystemEnvironmental scienceAmmoniaNitrogen dioxideAtmospheric sciencesNutrientLatitudeDry seasonEnvironmental chemistryChemistryEcologyGeographyGeologyMeteorologyBiology

Abstract

fetched live from OpenAlex

Abstract Reactive nitrogen (N r ) is an essential nutrient to plants and a limiting element for growth in many ecosystems, but it can have harmful effects on ecosystems when in excess. Satellite‐derived surface observations are used together with a dry deposition model to estimate the dry deposition flux of the most abundant short‐lived nitrogen species, NH 3 and NO 2 , over North America during the 2013 warm season. These fluxes demonstrate that the NH 3 contribution dominates over NO 2 for most regions (comprising ~85% of their sum in Canada and ~65% in the U.S.), with some regional exceptions (e .g. Alberta and northeastern U.S.). Nationwide, ~1.35 Tg of N from these species were dry deposited in the contiguous U.S., more than double the ~0.61 Tg in Canada (excluding territories) over this period. Forest fires are shown to be the major contributor of dry deposition of N r from NH 3 in northern latitudes, leading to deposition fluxes 2–3 times greater than from expected amounts without fires.

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 score0.225
Threshold uncertainty score0.971

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 studies0.0000.001
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 head0.034
GPT teacher head0.271
Teacher spread0.238 · 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

Citations111
Published2017
Admission routes3
Has abstractyes

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