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Record W2793519753 · doi:10.1038/s41598-018-20404-2

A Global Perspective on Sulfur Oxide Controls in Coal-Fired Power Plants and Cardiovascular Disease

2018· article· en· W2793519753 on OpenAlexaff
Cheng-Kuan Lin, Ro‐Ting Lin, Pi‐Cheng Chen, Pu Wang, Nathalie de Marcellis-Warin, Corwin Zigler, David C. Christiani

Bibliographic record

VenueScientific Reports · 2018
Typearticle
Languageen
FieldEnvironmental Science
TopicAir Quality and Health Impacts
Canadian institutionsPolytechnique Montréal
FundersHarvard Kennedy SchoolHarvard University
KeywordsSulfurDiseaseMedicineIncidence (geometry)Relative riskEnvironmental healthInternal medicineChemistryConfidence intervalMathematics

Abstract

fetched live from OpenAlex

Abstract Sulfur oxides (SOx), particularly SO2 emitted by coal-fired power plants, produce long-term risks for cardiovascular disease (CVD). We estimated the relative risks of CVD and ischemic heart disease (IHD) attributable to SOx emission globally. National SOx reduction achieved by emissions control systems was defined as the average SOx reduction percentage weighted by generating capacities of individual plants in a country. We analyzed the relative risk of CVD incidence associated with national SOx reduction for 13,581 coal-fired power-generating units in 79 countries. A 10% decrease in SOx emission was associated with 0.28% (males; 95%CI = −0.39%~0.95%) and 1.69% (females; 95%CI = 0.99%~2.38%) lower CVD risk. The effects on IHD were > 2 times stronger among males than females (2.78%, 95%CI = 1.99%~3.57% vs. 1.18%, 95%CI = 0.19%~2.17%). Further, 1.43% (males) and 8.00% (females) of CVD cases were attributable to suboptimal SOx reduction. Thus, enhancing regulations on SOx emission control represents a target for national and international intervention to prevent CVD.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.008
Threshold uncertainty score0.019

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0000.002
Scholarly communication0.0020.002
Open science0.0010.001
Research integrity0.0030.003
Insufficient payload (model declined to judge)0.0060.000

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 head0.023
GPT teacher head0.292
Teacher spread0.269 · 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 source (direct Gemma or distilled Codex), 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

Citations54
Published2018
Admission routes1
Has abstractyes

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