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Record W3201791494 · doi:10.1038/s43247-021-00277-6

River winds and pollutant recirculation near the Manaus city in the central Amazon

2021· article· en· W3201791494 on OpenAlexfundno aff
Tianning Zhao, Jianhuai Ye, Igor Oliveira Ribeiro, Yongjing Ma, Hui‐Ming Hung, Carla E. Batista, Matthew Stewart, Patrícia C. Guimarães, Jordi Vilà-Guerau De Arellano, Rodrigo Augusto Ferreira De Souza, Alex Guenther, Scot T. Martin

Bibliographic record

VenueCommunications Earth & Environment · 2021
Typearticle
Languageen
FieldEnvironmental Science
TopicAir Quality and Health Impacts
Canadian institutionsnot available
FundersDivision of Atmospheric and Geospace SciencesConselho Nacional de Desenvolvimento Científico e TecnológicoNatural Sciences and Engineering Research Council of CanadaCoordenação de Aperfeiçoamento de Pessoal de Nível SuperiorDivision of Graduate EducationCamille and Henry Dreyfus FoundationNational Science Foundation
KeywordsEnvironmental scienceAmazon rainforestAir quality indexPollutantHydrology (agriculture)Atmospheric sciencesWind speedOzoneAir pollutionMeteorologyGeographyGeologyEcology

Abstract

fetched live from OpenAlex

Abstract Local atmospheric recirculation flows (i.e., river winds) induced by thermal contrast between wide Amazon rivers and adjacent forests could affect pollutant dispersion, but observational platforms for investigating this possibility have been lacking. Here we collected daytime vertical profiles of meteorological variables and chemical concentrations up to 500 m with a copter-type unmanned aerial vehicle during the 2019 dry season. Cluster analysis showed that a river-forest recirculation flow occurred for 23% (13 of 56) of the profiles. In fair weather, the thermally driven river winds fully developed for synoptic wind speeds below 4 m s −1 , and during these periods the vertical profiles of carbon monoxide and total oxidants (defined as ozone and nitrogen dioxide) were altered. Numerical modeling shows that the river winds can recirculate pollution back toward the riverbank. There are implications regarding air quality for the many human settlements along the rivers throughout northern Brazil.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.219
Threshold uncertainty score0.643

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.060
GPT teacher head0.285
Teacher spread0.225 · 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 teacher head, 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

Citations14
Published2021
Admission routes1
Has abstractyes

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