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Record W3165763021 · doi:10.1038/s42003-021-02212-z

Zinc isotopes from archaeological bones provide reliable trophic level information for marine mammals

2021· article· en· W3165763021 on OpenAlexafffund
Jeremy McCormack, Paul Szpak, Nicolas Bourgon, Michael P. Richards, Corrie Hyland, Pauline Méjean, Jean‐Jacques Hublin, Klervia Jaouen

Bibliographic record

VenueCommunications Biology · 2021
Typearticle
Languageen
FieldEnvironmental Science
TopicIsotope Analysis in Ecology
Canadian institutionsSimon Fraser UniversityTrent University
FundersSocial Sciences and Humanities Research Council of CanadaMax-Planck-Institut für Evolutionäre AnthropologieMax-Planck-GesellschaftDeutsche Forschungsgemeinschaft
KeywordsTrophic levelδ15NIsotope analysisFood webArcticIsotopes of nitrogenEcologyStable isotope ratioUrsus maritimusδ13CIsotopeIsotopes of carbonPredationFood chainEnvironmental scienceBiologyTotal organic carbon

Abstract

fetched live from OpenAlex

Abstract In marine ecology, dietary interpretations of faunal assemblages often rely on nitrogen isotopes as the main or only applicable trophic level tracer. We investigate the geographic variability and trophic level isotopic discrimination factors of bone zinc 66 Zn/ 64 Zn ratios (δ 66 Zn value) and compared it to collagen nitrogen and carbon stable isotope (δ 15 N and δ 13 C) values. Focusing on ringed seals ( Pusa hispida ) and polar bears ( Ursus maritimus ) from multiple Arctic archaeological sites, we investigate trophic interactions between predator and prey over a broad geographic area. All proxies show variability among sites, influenced by the regional food web baselines. However, δ 66 Zn shows a significantly higher homogeneity among different sites. We observe a clear trophic spacing for δ 15 N and δ 66 Zn values in all locations, yet δ 66 Zn analysis allows a more direct dietary comparability between spatially and temporally distinct locations than what is possible by δ 15 N and δ 13 C analysis alone. When combining all three proxies, a more detailed and refined dietary analysis is possible.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.776
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.002
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.001

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 head0.043
GPT teacher head0.284
Teacher spread0.241 · 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

Citations46
Published2021
Admission routes2
Has abstractyes

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