MétaCan
Menu
Back to cohort
Record W2982034560 · doi:10.1126/sciadv.aaw4018

Volcanic mercury and mutagenesis in land plants during the end-Triassic mass extinction

2019· article· en· W2982034560 on OpenAlexaff
Sofie Lindström, Hamed Sanei, Bas van de Schootbrugge, Gunver Krarup Pedersen, Charles E. Lesher, Christian Tegner, Carmen Heunisch, Karen Dybkjær, P.M. Outridge

Bibliographic record

VenueScience Advances · 2019
Typearticle
Languageen
FieldEnvironmental Science
TopicMercury impact and mitigation studies
Canadian institutionsGeological Survey of CanadaNatural Resources Canada
FundersNederlandse Organisatie voor Wetenschappelijk OnderzoekDanmarks GrundforskningsfondNational Research FoundationNationale Geologiske Undersøgelser for Danmark og GrønlandGeocenter Danmark
KeywordsMercury (programming language)Extinction eventVolcanoExtinction (optical mineralogy)Environmental scienceEarth scienceBiologyGeologyEnvironmental chemistryChemistryPaleontologyComputer scienceMedicineEnvironmental health

Abstract

fetched live from OpenAlex

, and halocarbons are generally considered as major factors in the biotic crises, resulting in global warming, acid deposition, and ozone layer depletion. Here, we show that pulsed elevated concentrations of mercury in marine and terrestrial sediments across the Triassic-Jurassic boundary in southern Scandinavia and northern Germany correlate with intense volcanic activity in the Central Atlantic Magmatic Province. The increased levels of mercury-the most genotoxic element on Earth-also correlate with high occurrences of abnormal fern spores, indicating severe environmental stress and genetic disturbance in the parent plants. We conclude that this offers compelling evidence that emissions of toxic volcanogenic substances contributed to the end-Triassic biotic crisis.

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 score0.158
Threshold uncertainty score0.525

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.009
GPT teacher head0.248
Teacher spread0.239 · 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

Citations134
Published2019
Admission routes1
Has abstractyes

Explore more

Same venueScience AdvancesSame topicMercury impact and mitigation studiesFrench-language works237,207