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Record W1884323979 · doi:10.1038/srep14679

Chronic mercury exposure in Late Neolithic/Chalcolithic populations in Portugal from the cultural use of cinnabar

2015· article· en· W1884323979 on OpenAlexaff
Steven D. Emslie, Rebecka L. Brasso, William P. Patterson, António Carlos Valera, Ashley McKenzie, Ana María Silva, James D. Gleason, Joel D. Blum

Bibliographic record

VenueScientific Reports · 2015
Typearticle
Languageen
FieldEnvironmental Science
TopicMercury impact and mitigation studies
Canadian institutionsUniversity of Saskatchewan
FundersUniversity of North Carolina Wilmington
KeywordsCinnabarMercury (programming language)PrehistoryChalcolithicArchaeologyPopulationδ34SBronze AgeChemistryGeographyGeologyMineralogyEnvironmental healthQuartzMedicine

Abstract

fetched live from OpenAlex

Cinnabar is a natural mercury sulfide (HgS) mineral of volcanic or hydrothermal origin that is found worldwide. It has been mined prehistorically and historically in China, Japan, Europe, and the Americas to extract metallic mercury (Hg(0)) for use in metallurgy, as a medicinal, a preservative, and as a red pigment for body paint and ceramics. Processing cinnabar via combustion releases Hg(0) vapor that can be toxic if inhaled. Mercury from cinnabar can also be absorbed through the gut and skin, where it can accumulate in organs and bone. Here, we report moderate to high levels of total mercury (THg) in human bone from three Late Neolithic/Chalcolithic (5400-4100 B.P.) sites in southern Portugal that were likely caused by cultural use of cinnabar. We use light stable isotope and Hg stable isotope tracking to test three hypotheses on the origin of mercury in this prehistoric human bone. We traced Hg in two individuals to cinnabar deposits near Almadén, Spain, and conclude that use of this mineral likely caused mild to severe mercury poisoning in the prehistoric population. Our methods have applications to bioarchaeological investigations worldwide, and for tracking trade routes and mobility of prehistoric populations where cinnabar use is documented.

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 score0.006
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
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 head0.100
GPT teacher head0.309
Teacher spread0.209 · 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

Citations77
Published2015
Admission routes1
Has abstractyes

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