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Record W2012322872 · doi:10.1164/rccm.201310-1924pp

Climate Change. A Global Threat to Cardiopulmonary Health

2014· review· en· W2012322872 on OpenAlexfundno aff
Mary B. Rice, George D. Thurston, John R. Balmes, Kent E. Pinkerton

Bibliographic record

VenueAmerican Journal of Respiratory and Critical Care Medicine · 2014
Typereview
Languageen
FieldEnvironmental Science
TopicClimate Change and Health Impacts
Canadian institutionsnot available
FundersNational Institute for Occupational Safety and HealthCalifornia National Primate Research CenterNational Institutes of HealthYork UniversityNational Heart, Lung, and Blood InstituteNational Institute of Environmental Health SciencesCalifornia Air Resources BoardAmerican Thoracic Society
KeywordsClimate changePublic healthGlobal healthMedicineDisadvantagedPerspective (graphical)Global warmingEnvironmental healthNatural resource economicsEconomic growthEcologyEconomics

Abstract

fetched live from OpenAlex

Recent changes in the global climate system have resulted in excess mortality and morbidity, particularly among susceptible individuals with preexisting cardiopulmonary disease. These weather patterns are projected to continue and intensify as a result of rising CO2 levels, according to the most recent projections by climate scientists. In this Pulmonary Perspective, motivated by the American Thoracic Society Committees on Environmental Health Policy and International Health, we review the global human health consequences of projected changes in climate for which there is a high level of confidence and scientific evidence of health effects, with a focus on cardiopulmonary health. We discuss how many of the climate-related health effects will disproportionally affect people from economically disadvantaged parts of the world, who contribute relatively little to CO2 emissions. Last, we discuss the financial implications of climate change solutions from a public health perspective and argue for a harmonized approach to clean air and climate change policies.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.991
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0030.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
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 head0.104
GPT teacher head0.435
Teacher spread0.331 · 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 designOther design
Domainnot available
GenreReview

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

Citations72
Published2014
Admission routes1
Has abstractyes

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