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Record W4312043128 · doi:10.3389/fevo.2022.953042

Freshwater wetland–driven variation in sulfur isotope compositions: Implications for human paleodiet and ecological research

2022· article· en· W4312043128 on OpenAlexafffund
Eric Guiry, Trevor J. Orchard, Suzanne Needs‐Howarth, Paul Szpak

Bibliographic record

VenueFrontiers in Ecology and Evolution · 2022
Typearticle
Languageen
FieldEnvironmental Science
TopicIsotope Analysis in Ecology
Canadian institutionsToronto ZooUniversity of TorontoTrent University
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsWetlandEcologyEcosystemHabitatIsotope analysisGeographyBiology

Abstract

fetched live from OpenAlex

Sulfur isotope ( δ 34 S) analyses are an important archaeological and ecological tool for understanding human and animal migration and diet, but δ 34 S can be difficult to interpret, particularly in archaeological human-mobility studies, when measured isotope compositions are strongly 34 S-depleted relative to regional baselines. Sulfides, which accumulate under anoxic conditions and have distinctively low δ 34 S, are potentially key for understanding this but are often overlooked in studies of vertebrate δ 34 S. We analyze an ecologically wide range of archaeological taxa to build an interpretive framework for understanding the impact of sulfide-influenced δ 34 S on vertebrate consumers. Results provide the first demonstration that δ 34 S of higher-level consumers can be heavily impacted by freshwater wetland resource use. This source of δ 34 S variation is significant because it is linked to a globally distributed habitat and occurs at the bottom of the δ 34 S spectrum, which, for archaeologists, is primarily used for assessing human mobility. Our findings have significant implications for rethinking traditional interpretive frameworks of human mobility and diet, and for exploring the historical ecology of past freshwater wetland ecosystems. Given the tremendous importance of wetlands’ ecosystem services today, such insights on the structure and human dynamics of past wetlands could be valuable for guiding restoration work.

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 score0.060
Threshold uncertainty score0.885

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
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 head0.020
GPT teacher head0.282
Teacher spread0.263 · 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
Published2022
Admission routes2
Has abstractyes

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