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Record W4295067102 · doi:10.1126/sciadv.abo0866

Reduced plate motion controlled timing of Early Jurassic Karoo-Ferrar large igneous province volcanism

2022· article· en· W4295067102 on OpenAlexaff
Micha Ruhl, Stephen P. Hesselbo, Hugh C. Jenkyns, Weimu Xu, Ricardo Silva, Kara J. Matthews, Tamsin A. Mather, Conall Mac Niocaill, James B. Riding

Bibliographic record

VenueScience Advances · 2022
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGeological and Geochemical Analysis
Canadian institutionsUniversity of CalgaryUniversity of Manitoba
FundersNatural Environment Research CouncilBritish Geological SurveyNational Natural Science Foundation of ChinaSight Research UKScience Foundation IrelandUnited Nations Educational, Scientific and Cultural Organization
KeywordsVolcanismIgneous rockGeologyPaleontologySeismologyMotion (physics)GeochemistryEarth sciencePhysicsTectonics

Abstract

fetched live from OpenAlex

Past large igneous province (LIP) emplacement is commonly associated with mantle plume upwelling and led to major carbon emissions. One of Earth's largest past environmental perturbations, the Toarcian oceanic anoxic event (T-OAE; ~183 Ma), has been linked to Karoo-Ferrar LIP emplacement. However, the role of mantle plumes in controlling the onset and timing of LIP magmatism is poorly understood. Using global plate reconstruction models and Lower Toarcian sedimentary mercury (Hg) concentrations, we demonstrate (i) that the T-OAE occurred coevally with Karoo-Ferrar emplacement and (ii) that timing and duration of LIP emplacement was governed by reduced Pangean plate motion, associated with a reversal in plate movement direction. This new model mechanistically links Earth's interior and surficial processes, and the mechanism is consistent with the timing of several of the largest LIP volcanic events throughout Earth history and, thus, the timing of many of Earth's past global climate change and mass extinction events.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.343
Threshold uncertainty score0.997

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 studies0.0010.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.000

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 head0.011
GPT teacher head0.220
Teacher spread0.209 · 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.

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

Citations34
Published2022
Admission routes1
Has abstractyes

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