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Record W2614416570 · doi:10.1086/691683

Ancient Vitamin D Deficiency: Long-Term Trends

2017· article· en· W2614416570 on OpenAlexafffund
Megan B. Brickley, Lori D’Ortenzio, Bonnie Kahlon, Annabelle Schattmann, Isabelle Ribot, Émeline Raguin, Benoît Bertrand

Bibliographic record

VenueCurrent Anthropology · 2017
Typearticle
Languageen
FieldMedicine
TopicVitamin D Research Studies
Canadian institutionsMcMaster UniversityNatural Sciences and Engineering Research Council
FundersCanada Research ChairsMcMaster University
Keywordsvitamin D deficiencyRicketsVitamin deficiencyVitamin D and neurologyVitamin A deficiencyPhysiologyBiologyVitaminEndocrinologyRetinol

Abstract

fetched live from OpenAlex

Vitamin D deficiency is now widely recognized as one of the most common health conditions in the world, with important consequences for overall health. Levels of deficiency appear to be rising, but the extent to which past humans were affected by vitamin D deficiency and the roles of this hormone in past human health are currently unknown. The discovery that mineralization defects in tooth dentin reflect periods of deficiency and are preserved in our earliest ancestors offers a unique opportunity to provide information on past social and cultural organization and, with further work, to contribute to ongoing debates on change in skin pigmentation. Here we show that humans from some of the earliest Middle Eastern and European communities were affected by deficiency, but levels and severity appear to have increased notably through time. On a simple comparative scale, severity of deficiency was four times as high in Greek communities in 1948 CE as in early farming communities from ca. 3000 BCE; some individuals in the later periods would have had rickets. Research using interglobular dentin in humans and nonhuman primates has the potential to fill in many important gaps in understanding past and present aspects of vitamin D deficiency.

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 categoriesScience and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.323
Threshold uncertainty score1.000

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 studies0.0010.004
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.074
GPT teacher head0.435
Teacher spread0.360 · 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 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

Citations27
Published2017
Admission routes2
Has abstractyes

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