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Record W2313573185 · doi:10.1021/es204135s

Connecting Breeding and Wintering Habitats of Migratory Piscivorous Birds: Implications for Tracking Contaminants (Hg) Using Multiple Stable Isotopes

2012· article· en· W2313573185 on OpenAlexaffabout
Amy F.A. Ofukany, Keith A. Hobson, Leonard I. Wassenaar

Bibliographic record

VenueEnvironmental Science & Technology · 2012
Typearticle
Languageen
FieldEnvironmental Science
TopicIsotope Analysis in Ecology
Canadian institutionsEnvironment and Climate Change CanadaUniversity of Saskatchewan
FundersInternational Atomic Energy Agency
KeywordsFeatherHabitatIsotope analysisEcologyBiologyStable isotope ratioBiomeFisheryEnvironmental scienceEcosystem

Abstract

fetched live from OpenAlex

Contaminant concentrations in migratory species are complicated by differential accumulation and elimination among geographically separated biomes. Double-crested cormorants ( Phalacrocorax auritus ) are used as monitors of Hg in North America; however, migration from northern breeding colonies to southern marine, freshwater, and aquaculture systems exposes individuals to spatiotemporal variations in contaminant uptake. We used stable isotopes (δ(34)S, δ(13)C, δ(15)N, δ(2)H) in primary feathers and a combined Bayesian assignment and isotopic threshold model to identify feather origins and the potential winter use of aquaculture (δ(34)S < 10‰, δ(13)C > -14‰), freshwater (δ(34)S < 10‰, δ(13)C < -20‰), and marine habitats (δ(34)S > 10‰). This approach allowed us to contrast body burden Hg derived from the breeding and wintering grounds, as well as from marine and freshwater habitats. We found feathers grown on Lake Winnipeg had greater Hg concentrations (mean = 4.26 ± 1.47 μg/g fresh weight; n = 20) than winter-grown feathers (3.19 ± 1.64 μg/g; n = 19), but Hg in winter-grown feathers was not related to any specific habitat. Isotopic assays of tissues of migratory birds allowed the source and degree of contaminant exposure to be identified throughout the annual cycle.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.089
Threshold uncertainty score0.773

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.002
Scholarly communication0.0000.001
Open science0.0010.001
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 head0.020
GPT teacher head0.262
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.

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

Citations39
Published2012
Admission routes2
Has abstractyes

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