MétaCan
Menu
Back to cohort
Record W4400459694 · doi:10.1175/bams-d-23-0098.1

East African Megacity Air Quality: Rationale and Framework for a Measurement and Modeling Program

2024· article· en· W4400459694 on OpenAlexaff
Solomon Bililign, Steven S. Brown, Daniel M. Westervelt, Rajesh Kumar, Wenfu Tang, F. Flocke, William Vizuete, Kassahun Ture, Francis D. Pope, Belay Demoz, Akua Asa-Awuku, P. F. Levelt, Egide Kalisa, Garima Raheja, Alex Ndyabakira, Michael Gatari

Bibliographic record

VenueBulletin of the American Meteorological Society · 2024
Typearticle
Languageen
FieldEnvironmental Science
TopicAir Quality Monitoring and Forecasting
Canadian institutionsWestern University
FundersCarnegie Mellon UniversityNational Oceanic and Atmospheric AdministrationNational Science Foundation
KeywordsMegacityAir quality indexQuality (philosophy)Environmental scienceGeographyMeteorologyClimatologyGeologyEconomicsEconomy

Abstract

fetched live from OpenAlex

Abstract Air pollution in Africa is a significant public health issue responsible for 1.1 million premature deaths annually. Sub-Saharan Africa has the highest rate of population growth and urbanization of any region in the world, with substantial potential for future emission growth and worsening air quality. Accurate and extensive observations of meteorology and atmospheric composition have underpinned successful air pollution mitigation strategies in the Global North, yet Africa in general and East Africa in particular remain among the most sparsely observed regions in the world. This paper is based on the discussion of these issues during two international workshops, one held virtually in the United States in July 2021 and one in Kigali, Rwanda, in January 2023. The workshops were designed to develop a measurement, capacity building, and collaboration strategy to improve air quality-relevant measurements, modeling, and data availability in East Africa. This paper frames the relevant scientific needs and describes the requirements for training and infrastructure development for an integrated observing and modeling strategy that includes partnerships between East African scientists and organizations and their counterparts in the developed world. Significance Statement Air pollution is a leading environmental risk factor in East Africa that is expected to worsen with rapid urbanization and economic growth occurring in the region. The unique emission sources will impact atmospheric composition and chemistry and are of significant current interest to understand their impact on climate and air quality mitigation efforts everywhere. There is a need to quantify emission trends from different regions of the world and develop reliable methods for inventories. Relationships between scientists from both the Global North and the Global South will help to advance and implement measurements and to build global atmospheric chemistry capacity.

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)0.014
metaresearch head score (Gemma)0.012
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.024
Threshold uncertainty score0.074

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0140.012
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0030.003
Science and technology studies0.0030.005
Scholarly communication0.0060.004
Open science0.0030.007
Research integrity0.0030.005
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 head0.090
GPT teacher head0.313
Teacher spread0.223 · 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 designTheoretical or conceptual
Domainnot available
GenreMethods

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

Citations15
Published2024
Admission routes1
Has abstractyes

Explore more

Same venueBulletin of the American Meteorological SocietySame topicAir Quality Monitoring and ForecastingFrench-language works237,207