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Record W2794169666 · doi:10.3389/fpubh.2018.00095

Severe Urban Outdoor Air Pollution and Children’s Structural and Functional Brain Development, From Evidence to Precautionary Strategic Action

2018· article· en· W2794169666 on OpenAlexaff
Amedeo D’Angiulli

Bibliographic record

VenueFrontiers in Public Health · 2018
Typearticle
Languageen
FieldEnvironmental Science
TopicAir Quality and Health Impacts
Canadian institutionsCarleton University
Fundersnot available
KeywordsScientific evidencePerspective (graphical)CognitionPsychologyAction (physics)Air pollutionNeuroinflammationBrain developmentSet (abstract data type)PerceptionNeuroscienceEnvironmental healthMedicineDevelopmental psychologyBiologyPathologyDiseaseEcology

Abstract

fetched live from OpenAlex

According to the latest estimates, about 2 billion children around the world are exposed to severe urban outdoor air pollution. Transdisciplinary, multi-method findings from epidemiology, developmental neuroscience, psychology, and pediatrics, show detrimental outcomes associated with pre- and postnatal exposure are found at all ages. Affected brain-related functions include perceptual and sensory information processing, intellectual and cognitive development, memory and executive functions, emotion and self-regulation, and academic achievement. Correspondingly, with the breakdown of natural barriers against entry and translocation of toxic particles in the brain, the most common structural changes are responses promoting neuroinflammation and indicating early neurodegenerative processes. In spite of the gaps in current scientific knowledge and the challenges posed by non-scientific issues that influence policy, the evidence invites the conclusion that urban outdoor air pollution is a serious threat to healthy brain development which may set the conditions for neurodegenerative diseases. Such evidence supports the perspective that urgent strategic precautionary actions, minimizing exposure and attenuating its effects, are needed to protect children and their brain development.

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 score0.244
Threshold uncertainty score0.630

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 studies0.0010.000
Scholarly communication0.0000.001
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 head0.090
GPT teacher head0.315
Teacher spread0.226 · 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

Citations43
Published2018
Admission routes1
Has abstractyes

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