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Record W2970519405 · doi:10.1109/mele.2019.2925765

The Missing Health Link: How a transition to electrified vehicles may benefit more than just the environment

2019· article· en· W2970519405 on OpenAlex
Sloane Kowal, Abhay Dhand, Himanshi Khurana, Afreen Ahmad, Ali Emadi

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueIEEE Electrification Magazine · 2019
Typearticle
Languageen
FieldEnvironmental Science
TopicAir Quality and Health Impacts
Canadian institutionsMcMaster University
Fundersnot available
KeywordsMedicineEnvironmental healthCardiovascular healthAsthmaDiseaseParticulatesIntensive care medicinePathologyInternal medicineBiologyEcology

Abstract

fetched live from OpenAlex

Worldwide, more than 1 billion vehicles are used regularly, with the majority being gasoline powered. These transport methods are known emitters of carbon dioxide, particulate matter (PM), and other pollutants (i.e., nitric oxides). Such compounds pose a great environmental risk, but recent research has also suggested health consequences. PM, a microscopic carcinogenic substance, affects many biological systems and has been associated with medical concerns in clinical and laboratory settings. In clinical settings, research on the effects of PM of 25 pm or lower in diameter (PM25) have focused on interactions with the cardiovascular, respiratory, and nervous systems. Vulnerable populations (i.e., the elderly and hospitalized patients) disproportionately experience an increase in cardiovascular and respiratory deaths along with hospital admissions for heart disease and asthma. Also, studies have found an increase in tumorigenesis.

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.

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)0.002
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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.704
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.002

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 head0.033
GPT teacher head0.291
Teacher spread0.258 · 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