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Record W4361272597 · doi:10.18280/ijsse.130119

Short-Lived Climate Forcers in Urban City of Semarang Based on Land Use Category and Associated Health Risks

2023· article· en· W4361272597 on OpenAlexvenueno aff
Haryono Setiyo Huboyo, Okto Risdianto Manullang, Budi Prasetyo Samadikun

Bibliographic record

VenueInternational Journal of Safety and Security Engineering · 2023
Typearticle
Languageen
FieldMedicine
TopicPublic Health and Nutrition
Canadian institutionsnot available
FundersDirecció General de Recerca, Generalitat de Catalunya
KeywordsEnvironmental healthEnvironmental planningHealth riskGeographyMedicine

Abstract

fetched live from OpenAlex

Control of short-lived climate forcers (SLCF) is very crucial because it has quicker impact and more visible benefits in the short-term than control of carbon dioxide.For this reason, this research is aimed at estimating the level of SLCF (black carbon/BC, methane, and ozone) in the atmosphere, their impact and associated health risk (of certain SLCF).We measured BC at PM2.5, ground level ozone in 24h, and CH4, N2O and CO2 in five different sites in different land use categories.We also distributed questionnaires to vulnerable populations in the study area.The concentration of BC was in the range of 2.42-5.52ug/m 3 .The proportion of BC in PM2.5 is between 4.9% and 12.74%.Ambient methane concentrations were higher in the morning than in the afternoon.Likewise, the oxidant concentration of O3, N2O, and CO2 showed that results of measurement were higher in the morning than in the afternoon.The average concentration ratio of short-term to longterm greenhouse gas (CH4: N2O: CO2) is 4.9:1:1304.In terms of health risk, Estimated Attribute Proportion (AP) resulting from BC is within 1.1% and 7.48%.Mitigation of short-term GHG pollutant emission sources would be in line with long-term GHG pollutant mitigation.

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 score0.077
Threshold uncertainty score0.279

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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.039
GPT teacher head0.320
Teacher spread0.282 · 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

Citations0
Published2023
Admission routes1
Has abstractyes

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