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Record W7084920018 · doi:10.5281/zenodo.15754758

INMS Guidance Document on Nitrogen Impact Assessment Methods

2025· article· en· W7084920018 on OpenAlexaff

Bibliographic record

VenueNERC Open Research Archive (Natural Environment Research Council) · 2025
Typearticle
Languageen
FieldMedicine
TopicSchizophrenia research and treatment
Canadian institutionsPlant Biotechnology Institute
Fundersnot available
KeywordsNitrogenImpact assessmentEnvironmental impact assessmentReactive nitrogenIndex (typography)Quantitative assessment

Abstract

fetched live from OpenAlex

This INMS Guidance Document on Impact Assessment Methods provides practical guidelines for assessing beneficial and detrimental nitrogen (N) impacts on the environment and humans at all scales from local to regional to global. Written with input from scientists worldwide, this Guidance Document serves as a foundation for improved integrated nitrogen assessment and policy support. The Guidance Document introduces concepts of reactive N impacts and provides a framework that describes transformation processes of N drivers, pressures and impacts to describe and analyze the positive and negative effects of altered reactive N cycles in different environments. We introduce a comprehensive Nitrogen Matrix of Impacts and Pressures (N-MIP), which is an interactive tool to link N impacts with brief information on underlying mechanisms and determine the category of the impacts. Six different integrated methodologies provide policy makers and other practitioners ways of examining the pathways and trade-offs involved with nitrogen fluxes. These cover: nitrogen budgets, nitrogen footprints, nitrogen use efficiency, planetary boundaries, critical loads, environmental performance index and cost-benefit assessments. The Guidance Document concludes with national or sub-national examples that illustrate how nitrogen impact methodologies have been applied.

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)0.047
metaresearch head score (Gemma)0.009
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Science and technology studies, Scholarly communication, Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesMetaresearch, Insufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.544
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0470.009
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0020.002
Scholarly communication0.0010.001
Open science0.0030.006
Research integrity0.0000.006
Insufficient payload (model declined to judge)0.0030.001

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 head0.149
GPT teacher head0.506
Teacher spread0.357 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
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

Citations0
Published2025
Admission routes1
Has abstractyes

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