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Record W4407472088 · doi:10.1139/as-2024-0043

Exploring 4200 Years of Mercury Variation in the Antlers of High-Arctic Wild Reindeer

2025· article· en· W4407472088 on OpenAlexvenueno aff
Saria Sato-Bajracharya, Mathilde Le Moullec, Brage Bremset Hansen, Bjørn Munro Jenssen, Tomasz Maciej Ciesielski

Bibliographic record

VenueArctic Science · 2025
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGeology and Paleoclimatology Research
Canadian institutionsnot available
Fundersnot available
KeywordsArcticMercury (programming language)Variation (astronomy)The arcticGeographyEnvironmental sciencePhysical geographyBiologyEcologyOceanographyGeologyComputer scienceAstronomy

Abstract

fetched live from OpenAlex

Anthropogenic mercury (Hg) emissions and climate change are altering the global cycle of Hg. Levels of Hg in natural archives can help us understand not only the historical trends but also the future changes in different ecosystems, including the Arctic. In this study, we investigated the temporal variation of Hg across 4200 years in 78 antlers of the high-Arctic Svalbard reindeer (Rangifer tarandus platyrhynchus). Antler Hg concentrations were higher during the pre-1650 Anno Domini (AD) period compared to the post-1650 AD period. Thus, antler Hg concentrations did not reflect the increased environmental Hg levels caused by high anthropogenic activities during the past three centuries. Trabecular Hg concentrations tended to be higher during the Medieval Warm Period than during the Little Ice Age, as revealed by a post-hoc analysis conducted to explore the relationship between climatic variation and antler Hg concentrations. The overall mean (±SE) antler Hg concentration was generally low (5.29 ± 0.46 ng/g) compared to the present levels of Hg in various tissues of terrestrial Arctic ungulates. Combined with other paleo-archives, this millennial-scale study of Hg using antlers could provide insights into the temporal patterns and potential drivers of Hg variation in Arctic terrestrial ecosystems.

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)0.002
metaresearch head score (Gemma)0.001
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 score0.003
Threshold uncertainty score0.680

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.052
GPT teacher head0.265
Teacher spread0.213 · 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

Citations0
Published2025
Admission routes1
Has abstractyes

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