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Record W4293105319 · doi:10.1080/10256016.2022.2113995

Stable sulphur isotope (<i>δ</i><sup>34</sup>S) ratios in bird feathers from India indicate strong segregation between the Himalaya and Gangetic plain, and the rest of India

2022· article· en· W4293105319 on OpenAlexaff
Yuvraj Date, Shreyas Managave, Girish Jathar, Rahul Khot, Keith A. Hobson

Bibliographic record

VenueIsotopes in Environmental and Health Studies · 2022
Typearticle
Languageen
FieldEnvironmental Science
TopicIsotope Analysis in Ecology
Canadian institutionsWestern UniversityEnvironment and Climate Change Canada
Fundersnot available
KeywordsFeatherδ34Sδ13CStable isotope ratioIsotope analysisBiologyEcologyδ15NRange (aeronautics)ZoologyPaleontology

Abstract

fetched live from OpenAlex

Although stable hydrogen (δ2H) and carbon (δ13C) isotopic compositions of bird feathers have been increasingly used to understand the migration of birds through assignment of individuals to established isoscapes, far less is known about the structure of feather isoscapes based on stable sulphur isotope (δ34S) assays. While δ2H isoscapes have been used to investigate the migratory origins of the Globe Skimmer (Pantala flavescens) dragonfly in India, no isotope studies have been applied to avian migration patterns in that region. Here, based on museum collections, we report the feather δ34S values of 13 avian species that included marine birds with expected 34S enriched feathers, resident birds throughout India and a single migratory species, Bar-headed Goose (Anser indicus). Feathers from the marine birds were the most enriched (20.6 ± 1.2 ‰, n = 10; range = 4.4 ‰) in 34S but terrestrial resident species also showed unexpectedly high δ34S values (11.7 ± 4.8 ‰, n = 74; range = 19.9 ‰) throughout most of India but the Himalayan region and Gangetic plain typically showed δ34S values less than 6.4 ‰. Our results show the utility of feather δ34S studies to decipher avian and other animal migration between these two regions.

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)0.000
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 score0.007
Threshold uncertainty score0.630

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.002
Scholarly communication0.0000.000
Open science0.0000.001
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 head0.018
GPT teacher head0.258
Teacher spread0.240 · 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

Citations4
Published2022
Admission routes1
Has abstractyes

Explore more

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