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Record W3108661131 · doi:10.1002/jqs.3262

A bio‐available strontium isoscape for eastern Beringia: a tool for tracking landscape use of Pleistocene megafauna

2020· article· en· W3108661131 on OpenAlexaff
Juliette Funck, Clément P. Bataille, Jeffrey T. Rasic, Matthew J. Wooller

Bibliographic record

VenueJournal of Quaternary Science · 2020
Typearticle
Languageen
FieldEnvironmental Science
TopicIsotope Analysis in Ecology
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsBeringiaMegafaunaPleistoceneFaunaGeologyProvenanceArchaeologyStrontiumPaleontologyEcologyPhysical geographyEarth scienceGeographyBiologyChemistry

Abstract

fetched live from OpenAlex

ABSTRACT Numerous paleoecological questions concern the mobility of ancient fauna in eastern Beringia. Strontium (Sr) isotope ratio ( 87 Sr/ 86 Sr) analysis has emerged as a powerful tracer for determining the provenance of ancient biological materials. However, it is important to characterize 87 Sr/ 86 Sr variation across a landscape. We measured the 87 Sr/ 86 Sr composition of teeth from present‐day, herbivorous rodents ( n = 162) sampled from across eastern Beringia to estimate bio‐available 87 Sr/ 86 Sr values. We compiled these data with the very limited number of previously published 87 Sr/ 86 Sr values from the region. We then used this dataset and a machine learning, random‐forest regression to predict bio‐available 87 Sr/ 86 Sr variations across eastern Beringia. As a case study using our new 87 Sr/ 86 Sr map (isoscape), we measured the 87 Sr/ 86 Sr and oxygen stable isotope values (δ 18 O) of five radiocarbon‐dated steppe bison from eastern Beringia and compared these to our 87 Sr/ 86 Sr isoscape and a δ 18 O isoscape to estimate the probable landscape use of these ancient fauna. Our model and isoscape provide important foundations for a wide range of additional applications, including studies of the paleo‐mobility of other fauna, ancient people and present‐day fauna in eastern Beringia.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.713
Threshold uncertainty score0.491

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.045
GPT teacher head0.264
Teacher spread0.218 · 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

Citations32
Published2020
Admission routes1
Has abstractyes

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