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Record W2966356672 · doi:10.1038/s41467-019-11453-w

Impacts of air pollutants from rural Chinese households under the rapid residential energy transition

2019· article· en· W2966356672 on OpenAlexfundno aff
Guofeng Shen, Muye Ru, Wei Du, Xi Zhu, Qirui Zhong, Yilin Chen, Huizhong Shen, Xiao Yun, Wenjun Meng, Junfeng Liu, Hefa Cheng, Jianying Hu, Dabo Guan, Shu Tao

Bibliographic record

VenueNature Communications · 2019
Typearticle
Languageen
FieldEnvironmental Science
TopicAir Quality and Health Impacts
Canadian institutionsnot available
FundersUniversity of North Carolina at Chapel HillNanjing UniversityDalhousie UniversityNational Natural Science Foundation of China
KeywordsParticulatesEnvironmental scienceAir pollutionPollutantAir quality indexRural populationChinaPopulationIndoor air qualityClimate changeAir pollutantsSolid fuelEnvironmental healthAerodynamic diameterEnvironmental protectionCombustionEnvironmental engineeringGeographyMeteorologyEcologyChemistryMedicine

Abstract

fetched live from OpenAlex

Abstract Rural residential energy consumption in China is experiencing a rapid transition towards clean energy, nevertheless, solid fuel combustion remains an important emission source. Here we quantitatively evaluate the contribution of rural residential emissions to PM 2.5 (particulate matter with an aerodynamic diameter less than 2.5 μm) and the impacts on health and climate. The clean energy transitions result in remarkable reductions in the contributions to ambient PM 2.5 , avoiding 130,000 (90,000–160,000) premature deaths associated with PM 2.5 exposure. The climate forcing associated with this sector declines from 0.057 ± 0.016 W/m 2 in 1992 to 0.031 ± 0.008 W/m 2 in 2012. Despite this, the large remaining quantities of solid fuels still contributed 14 ± 10 μg/m 3 to population-weighted PM 2.5 in 2012, which comprises 21 ± 14% of the overall population-weighted PM 2.5 from all sources. Rural residential emissions affect not only rural but urban air quality, and the impacts are highly seasonal and location dependent.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.724
Threshold uncertainty score1.000

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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.020
GPT teacher head0.305
Teacher spread0.285 · 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 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

Citations256
Published2019
Admission routes1
Has abstractyes

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