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Record W4327579988 · doi:10.14430/arctic76992

Mercury in Soils of Seabird Nesting Islands in West Iceland

2023· article· en· W4327579988 on OpenAlexfundvenueno aff
Mark L. Mallory, Aevar Petersen, Sverrir Thorstensen, Ian Spooner, Nelson J. O’Driscoll, Julia E. Baak, Jessie McIntyre

Bibliographic record

VenueARCTIC · 2023
Typearticle
Languageen
FieldEnvironmental Science
TopicMercury impact and mitigation studies
Canadian institutionsnot available
FundersCanada Research ChairsAcadia University
KeywordsSeabirdGuanoMercury (programming language)Soil waterδ15NEnvironmental scienceEcologyIsotope analysisArcticEnvironmental chemistryOceanographyStable isotope ratioChemistryGeologyBiologySoil scienceδ13CPredation

Abstract

fetched live from OpenAlex

Seabirds are globally recognized vectors of marine-derived materials, which get deposited on land at their breeding colonies, potentially altering local soil chemistry. We studied mercury (Hg) in soil cores on two islands in west Iceland that host thousands of nesting seabirds, predicting that Hg subsidies from nesting birds would result in elevated Hg in local soils. However, despite clear evidence from nitrogen isotopes of marine influence (seabird faeces) on coastal soil cores, O horizon Hg concentrations averaged 223 nanograms per gram (ng/g), were similar between reference and seabird-nesting sites, and were within the range of soils elsewhere in Europe and the Arctic. The concentration of Hg declined for samples deeper in the core, mirroring declines in organic content and concomitant increases in stable isotopes of nitrogen. A more detailed analysis of local pedogenic processes is required to determine the relative contribution of lithogenic, atmospheric, and anthropogenic Hg, but our data do not suggest that seabirds are markedly increasing local soil Hg through ornithogenic subsidies.

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)0.000
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 score0.008
Threshold uncertainty score0.482

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
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 head0.025
GPT teacher head0.274
Teacher spread0.250 · 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

Citations2
Published2023
Admission routes2
Has abstractyes

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