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Toward the improvement of total nitrogen deposition budgets in the United States

2019· article· en· W2958938104 on OpenAlexaff
John T. Walker, Gregory Beachley, Helen M. Amos, Jill S. Baron, Jesse O. Bash, Ralph E. Baumgardner, Michael D. Bell, Katherine Benedict, X. Chen, David W. Clow, Amanda Cole, Justin G. Coughlin, Karla Aparecida Vasconcelos Alves da Cruz, Ryan Daly, Stephen Decina, Emily M. Elliott, Mark E. Fenn, L. Ganzeveld, Kristi A. Gebhart, Selma S. Isil, Brian M. Kerschner, Robert S. Larson, Thomas F. Lavery, Gary Lear, Taylor R. Macy, M. Alisa Mast, K. Mishoe, Kristi Morris, P. Padgett, Rich Pouyat, Melissa Puchalski, Havala O. T. Pye, Anne W. Rea, Mark F. Rhodes, Christopher M. Rogers, Rick Saylor, Richard D. Scheffe, Bret A. Schichtel, Donna Schwede, G. A. Sexstone, B. C. Sive, Rodolfo Sosa Echeverría, Pamela H. Templer, Tammy M. Thompson, Daniel Tong, Gregory A. Wetherbee, Thomas H. Whitlow, Zhiyong Wu, Zhongjie Yu, Leiming Zhang

Bibliographic record

VenueThe Science of The Total Environment · 2019
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicAtmospheric chemistry and aerosols
Canadian institutionsEnvironment and Climate Change Canada
FundersNational Center For Environmental AssessmentU.S. Environmental Protection Agency
KeywordsDeposition (geology)NOxNitrogenEnvironmental scienceEcosystemEnvironmental chemistryReactive nitrogenChemistryEcologyGeologyBiologyCombustion

Abstract

fetched live from OpenAlex
No abstract in any covered source. Its absence is recorded, not treated as a negative.

No abstract. This is not a gap in this database; OpenAlex has none either. 23.3% of the frame is in this state, and the screen finds HALF as much metaresearch here, so the absence is a measured bias rather than a missing field.

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)0.002
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.248
Threshold uncertainty score0.493

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.007
GPT teacher head0.178
Teacher spread0.171 · 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 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

Citations70
Published2019
Admission routes1
Has abstractno

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