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Record W2049218191 · doi:10.2478/v10145-011-0030-2

Air Quality in Riga and Its Improvement Options

2011· article· en· W2049218191 on OpenAlexaff
Jānis Kleperis, Gunārs Bajārs, Ingrīda Brēmere, Martins Menniks, Artūrs Vīksna, Agnese Osīte, Dmitrijs Pavlicuks

Bibliographic record

VenueScientific Journal of Riga Technical University Environmental and Climate Technologies · 2011
Typearticle
Languageen
FieldEnvironmental Science
TopicAir Quality and Health Impacts
Canadian institutionsInternational Society of Electrophysiology and Kinesiology
Fundersnot available
KeywordsParticulatesAir quality indexEnvironmental scienceAir pollutionEuropean commissionPollutantNitrogen dioxidePollutionEnvironmental engineeringEnvironmental protectionWaste managementEuropean unionBusinessMeteorologyEngineeringGeographyChemistry

Abstract

fetched live from OpenAlex

Air Quality in Riga and Its Improvement Options Air quality in the city of Riga is evaluated from direct monitoring results and from accounting registered air pollutants in the city. It is concluded that from all air polluting substances listed in the European Commission directives, only nitrogen dioxide NO2 and particulate matter PM10 exceed the limits. In assessing the projected measures to improve air quality in Riga, it can be concluded that the implementation of cleaner fuels and improvements in energy efficiency of household and industrial sectors will decrease particle pollution, but measures in the transport sector will also contribute to reducing air pollution from nitrogen oxides.

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)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.162
Threshold uncertainty score0.323

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.002
Science and technology studies0.0010.001
Scholarly communication0.0030.001
Open science0.0010.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0040.001

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 head0.037
GPT teacher head0.250
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 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

Citations3
Published2011
Admission routes1
Has abstractyes

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