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Evaluation of operational on-line-coupled regional air quality models over Europe and North America in the context of AQMEII phase 2. Part I: Ozone

2014· article· en· W2055703682 on OpenAlexaff
Ulaş İm, Roberto Bianconi, Efisio Solazzo, Ioannis Kioutsioukis, Alba Badía, Alessandra Balzarini, Rocío Baró, Roberto Bellasio, Dominik Brunner, Charles Chemel, Gabriele Curci, Johannes Flemming, Renate Forkel, Lea Giordano, Pedro Jiménez‐Guerrero, Marcus Hirtl, Alma Hodžić, Luka Honzak, Oriol Jorba, Christoph Knote, Jeroen Kuenen, Paul A. Makar, Astrid Manders, Lucy Neal, Juan L. Pérez, Guido Pirovano, George Pouliot, Roberto San José, Nick Savage, Wolfram Schröder, Ranjeet S. Sokhi, Dimiter Syrakov, Alfreida Torian, Paolo Tuccella, Johannes Werhahn, Ralf Wolke, Khairunnisa Yahya, Rahela Žabkar, Yang Zhang, Junhua Zhang, Christian Hogrefe, Stefano Galmarini

Bibliographic record

VenueAtmospheric Environment · 2014
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicAtmospheric chemistry and aerosols
Canadian institutionsEnvironment and Climate Change Canada
FundersEuropean Regional Development FundAgenzia Spaziale ItalianaEuropean CommissionNational Center for Atmospheric ResearchNational Institute of EcologyU.S. Department of EnergyU.S. Environmental Protection AgencyMinisterstvo školstva, vedy, výskumu a športu Slovenskej republikyNational Science Foundation
KeywordsContext (archaeology)Air quality indexOzoneEnvironmental scienceClimatologyAtmospheric sciencesMeteorologyChemical transport modelGeographyGeology

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)0.003
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.146
Threshold uncertainty score0.290

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.004
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0020.001
Research integrity0.0020.001
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 head0.051
GPT teacher head0.264
Teacher spread0.213 · 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 designSimulation or modeling
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

Citations284
Published2014
Admission routes1
Has abstractno

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