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Record W2065427685 · doi:10.1021/es304351p

Characterization of Ultrafine Particulate Matter from Traditional and Improved Biomass Cookstoves

2013· article· en· W2065427685 on OpenAlexaff
Brian Just, Steven N. Rogak, Milind Kandlikar

Bibliographic record

VenueEnvironmental Science & Technology · 2013
Typearticle
Languageen
FieldEnvironmental Science
TopicEnergy and Environment Impacts
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsParticulatesUltrafine particleEnvironmental scienceBiomass (ecology)Characterization (materials science)Waste managementChemistryMaterials scienceEngineeringNanotechnologyGeology

Abstract

fetched live from OpenAlex

Biomass combustion in cookstoves has a substantial impact on human health, affects CO2 levels in the atmosphere, and black carbon (BC) and organic carbon (OC) affect the earth's radiative balance. Various initiatives propose to replace traditional fires with "improved" (nontraditional) cookstoves to offset negative local and global effects. In this laboratory study, we compared the size, composition, and morphology of ultrafine particulate emissions from a "three-stone" traditional fire to those from two improved stove designs (one "rocket", one "gasifier"). Measurement tools included a scanning mobility particle sizer, PTFE and quartz filter samples, and transmission electron microscopy. In the improved stoves, particulate mass (PM) emissions factors were much lower although median particle size was also lower: 35 and 24 nm for the rocket and gasifier, respectively, vs 61 nm for the three-stone fire. Particles from improved stoves formed clearly defined chain agglomerates and independent spheres with little evidence of volatile matter and had a higher proportion of BC to total PM, although overall BC emissions factors were fairly uniform. The 3-fold increase in quantities of sub-30 nm particles from improved cookstoves warrants further consideration by health scientists, with due consideration to the higher combustion efficiencies of improved cookstoves.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.438
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.003
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0090.001

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 head0.005
GPT teacher head0.167
Teacher spread0.161 · 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.

Study designBench or experimental
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

Citations89
Published2013
Admission routes1
Has abstractyes

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