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Record W1910373374

Drawing Down N2O to Protect Climate and the Ozone Layer: A UNEP Synthesis Report

2013· article· en· W1910373374 on OpenAlexaboutno aff
Lex Bouwman, J. S. Daniel, Eric A. Davidson, Cecile A. M. de Klein, Elisabeth A. Holland, Xiaotang Ju, David Kanter, O. Oenema, A. R. Ravishankara, Ute Skiba, Sietske van der Sluis, Mark A. Sutton, Guido R. van der Werf, Timothy J. Wallington, Peter Wiesen, Wilfried Winiwarter

Bibliographic record

VenueNERC Open Research Archive (Natural Environment Research Council) · 2013
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicClimate Change Policy and Economics
Canadian institutionsnot available
Fundersnot available
KeywordsNitrous oxideOzone layerOzone depletionGreenhouse gasEnvironmental scienceMontreal ProtocolClimate changeOzoneNatural resource economicsPollutantNitrogen oxideAtmosphere (unit)OxideLayer (electronics)Environmental protectionNOxMeteorologyChemistryEcologyEconomicsGeography
DOInot available

Abstract

fetched live from OpenAlex

Nitrous oxide is a potent pollutant that has both ozone layer-damaging properties and the ability to contribute to climate change. This dual characteristic makes nitrous oxide a gas that needs to be targeted for emissions reduction. This is especially the case, since its emission has been increasing since the preindustrial era. \n \nHowever, it could be argued that not enough attention has been given to reducing nitrous oxide emissions. UNEP has therefore developed this report with the aim of informing policymakers and stakeholders about the impacts of nitrous oxide emissions on climate and ozone layer and to present available opportunities for reducing emissions. \n \nThe report also articulates how efforts associated with reducing nitrous oxide emissions are closely linked to the concept of an inclusive “green economy”. \n \n

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 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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.066
Threshold uncertainty score0.131

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0020.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.003
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0010.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0060.001

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 head0.258
GPT teacher head0.338
Teacher spread0.080 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations72
Published2013
Admission routes1
Has abstractyes

Explore more

Same venueNERC Open Research Archive (Natural Environment Research Council)Same topicClimate Change Policy and EconomicsFrench-language works237,207