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Record W3026447126 · doi:10.1130/g47295.1

Toxic mercury pulses into late Permian terrestrial and marine environments

2020· article· en· W3026447126 on OpenAlexaff
Stephen E. Grasby, Xiaojun Liu, Runsheng Yin, Richard E. Ernst, Zhuoheng Chen

Bibliographic record

VenueGeology · 2020
Typearticle
Languageen
FieldEnvironmental Science
TopicMercury impact and mitigation studies
Canadian institutionsCarleton UniversityGeological Survey of CanadaNatural Resources Canada
Fundersnot available
KeywordsVolcanoExtinction eventMercury (programming language)Terrestrial ecosystemPermianLarge igneous provinceExtinction (optical mineralogy)GeologyEcosystemEnvironmental scienceAtmosphere (unit)Earth scienceAtmospheric sciencesPaleontologyEcologyMeteorologyBiologyGeography

Abstract

fetched live from OpenAlex

Abstract Large spikes in mercury (Hg) concentration are observed globally at the latest Permian extinction (LPE) horizon that are thought to be related to enhanced volcanic emissions of the Siberian Traps large igneous province (LIP). While forming an effective chemostratigraphic marker, it remains unclear whether such enhanced volcanic Hg emissions could have generated toxic conditions that contributed to extinction processes. To address this, we examined the nature of enhanced Hg emissions from the Siberian Traps LIP and the potential impact it may have had on global ecosystems during the LPE. Model results for a LIP eruption predict that pulses of Hg emissions to the atmosphere would have been orders of magnitude greater than normal background conditions. When deposited into world environments, this would have generated a series of toxic shocks, each lasting >1000 yr. Such repeated Hg loading events would have had severe impact across marine trophic levels, as well as been toxic to terrestrial plant and animal life. Such high Hg loading rates may help explain the co-occurrence of marine and terrestrial extinctions.

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 score0.007
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.017
GPT teacher head0.241
Teacher spread0.224 · 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

Citations97
Published2020
Admission routes1
Has abstractyes

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