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Record W2769586355 · doi:10.1038/s41598-017-16058-1

Impact of Multidecadal Climate Variability on United Kingdom Rickets Rates

2017· article· en· W2769586355 on OpenAlexafffund
Haris Majeed, G. W. K. Moore

Bibliographic record

VenueScientific Reports · 2017
Typearticle
Languageen
FieldEnvironmental Science
TopicClimate Change and Health Impacts
Canadian institutionsCollege of Family Physicians of CanadaCanada Research ChairsHospital for Sick ChildrenUniversity of Toronto
FundersEuropean Centre for Medium-Range Weather ForecastsUniversity of Toronto MississaugaMet OfficeUniversity of TorontoUniversity of Oxford
KeywordsRicketsClimate changeClimatologyEnvironmental scienceGeographyBiologyMedicineVitamin D and neurologyEcologyInternal medicineGeology

Abstract

fetched live from OpenAlex

Children who receive inadequate exposure to sunlight have reduced levels of vitamin D, resulting in rickets, a disease that is characterized by bone deformity, stunted growth, and long term pronounced disability. The United Kingdom rickets incidence rates declined from the early 1960's to mid-1990's, after which there was a dramatic increase. The reason for this change is not well understood. Here we show that an important low frequency mode of climate variability, the Atlantic Multidecadal Oscillation (AMO), with a period of ~60-80 years, has an impact on rickets incidence rates in the United Kingdom through changes in sea level pressure, cloud cover and sunshine duration. This research highlights the important role that multidecadal climate variability can play in human morbidity and suggests that future changes in the AMO may modulate rickets incidence rates throughout the United Kingdom.

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)0.000
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.042
Threshold uncertainty score0.084

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.093
GPT teacher head0.390
Teacher spread0.297 · 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

Citations10
Published2017
Admission routes2
Has abstractyes

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