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Record W2012096575 · doi:10.3329/jbas.v36i2.12970

Source Identification of Carbonaceous Aerosols During Winter Months in the Dhaka City

2012· article· en· W2012096575 on OpenAlexaff
Bilkis A. Begum, Kallol Kumar Roy, Fakrul Islam, Abdus Salam, Philip K. Hopke

Bibliographic record

VenueJournal of Bangladesh Academy of Sciences · 2012
Typearticle
Languageen
FieldEnvironmental Science
TopicAir Quality and Health Impacts
Canadian institutionsAtomic Energy (Canada)
Fundersnot available
KeywordsDiesel fuelGasolineAir quality indexEnvironmental scienceParticulatesEnvironmental chemistryEnvironmental engineeringWaste managementChemistryGeographyMeteorologyEngineering

Abstract

fetched live from OpenAlex

Air particulate matter samples were collected using Air Metrics samplers from 11 - 17 January and 19 - 27 January, 2012 at Amin Bazar and Farm Gate sites, respectively. The sampling time was from 8 a.m. - 4 p.m. Three samplers were used of which two samplers were for PM2.5 samples, using Teflon and quartz filters and the others for PM10 samples using Teflon filter. Organic and elemental carbons (OC and EC) were measured in PM2.5 samples at both sites. It has been found that the EC concentration at Amin Bazar is higher than in Farm Gate. The contribution of EC may come from diesel, gasoline and coal/wood combustions in the Amin Bazar site. The present OC/EC data were compared with the previous data. It was found that the concentration of EC became higher than those in the previous year. During last couple of years, Government implemented different policies specially in case of motor vehicles to improve the air quality. But due to the use of diesel in quick rental power plants, the air quality start to deteriorate. BC plays an important role to change the climate. Hence, government should think of alternatives to meet the power demand in place of diesel. DOI: http://dx.doi.org/10.3329/jbas.v36i2.12970 Journal of Bangladesh Academy of Sciences, Vol. 36, No. 2, 241-250, 2012

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)0.005
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 score0.058
Threshold uncertainty score0.269

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.327
Teacher spread0.277 · 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

Citations4
Published2012
Admission routes1
Has abstractyes

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