MétaCan
Menu
Back to cohort
Record W2016748965 · doi:10.1002/ajhb.20968

The health of Arctic populations: Does cold matter?

2009· article· en· W2016748965 on OpenAlexafffund
T. Kue Young, Tiina Mäkinen

Bibliographic record

VenueAmerican Journal of Human Biology · 2009
Typearticle
Languageen
FieldEnvironmental Science
TopicClimate Change and Health Impacts
Canadian institutionsPublic Health OntarioUniversity of Toronto
FundersCanadian Institutes of Health Research
KeywordsLife expectancyDemographyFertilityInfant mortalityArcticPublic healthTotal fertility ratePopulationMortality rateMedicineSocioeconomic statusBirth rateEnvironmental healthBiologyFamily planningEcology

Abstract

fetched live from OpenAlex

The objective of the study was to examine whether cold climate is associated with poorer health in diverse Arctic populations. With climate change increasingly affecting the Arctic, the association between climate and population health status is of public health significance. The mean January and July temperatures were determined for 27 Arctic regions based on weather station data for the period 1961-1990 and their association with a variety of health outcomes assessed by correlation and multiple linear regression analyses. Mean January temperature was inversely associated with infant and perinatal mortality rate, age-standardized mortality rate from respiratory diseases, and age-specific fertility rate for teens and directly associated with life expectancy at birth in both males and females, independent of a variety of socioeconomic, demographic, and health care factors. Mean July temperature was also associated with infant mortality and mortality from respiratory diseases, and with total fertility rate. For every 10 degrees C increase in mean January temperature, the life expectancy at birth among males increased by about 6 years and infant mortality rate decreased by about 4 deaths/1,000 livebirths. Cold climate is significantly associated with higher mortality and fertility in Arctic populations and should be recognized in public health planning.

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)0.000
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 score0.083
Threshold uncertainty score0.364

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.048
GPT teacher head0.369
Teacher spread0.322 · 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

Citations60
Published2009
Admission routes2
Has abstractyes

Explore more

Same venueAmerican Journal of Human BiologySame topicClimate Change and Health ImpactsFrench-language works237,207