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Record W2550343609 · doi:10.1130/g38487.1

Isotopic signatures of mercury contamination in latest Permian oceans

2016· article· en· W2550343609 on OpenAlexaff
Stephen E. Grasby, Wenjie Shen, Runsheng Yin, James D. Gleason, Joel D. Blum, Ryan F. Lepak, James P. Hurley, Benoı̂t Beauchamp

Bibliographic record

VenueGeology · 2016
Typearticle
Languageen
FieldEnvironmental Science
TopicMercury impact and mitigation studies
Canadian institutionsUniversity of CalgaryGeological Survey of CanadaNatural Resources Canada
FundersU.S. Geological SurveyUniversity of MichiganNational Science Foundation
KeywordsGeologyMercury (programming language)PermianContaminationEarth sciencePaleontologyOceanographyGeochemistryEcology

Abstract

fetched live from OpenAlex

Abstract Sedimentary records from the northwest margin of Pangea and the Tethys show anomalously high Hg levels at the latest Permian extinction boundary. Background δ202Hg values are consistent with normal marine conditions but exhibit negative shifts coincident with increased Hg concentrations. Hg isotope mass-independent fractionation (Δ199Hg) trends are consistent with volcanic input in deep-water marine environments. In contrast, nearshore environments have Δ199Hg signatures consistent with enhanced soil and/or biomass input. We hypothesize that the deep-water signature represents an overall global increase in volcanic Hg input and that this isotope signature is overwhelmed in nearshore locations due to Hg from terrestrial sources. High-productivity nearshore regions may have experienced stressed marine ecosystems due to enhanced Hg loading.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.010
Threshold uncertainty score0.021

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.010
GPT teacher head0.242
Teacher spread0.232 · 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 source (direct Gemma or distilled Codex), 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

Citations254
Published2016
Admission routes1
Has abstractyes

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