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Voluminous crustal degassing and immiscible sulfide genesis caused by magma-shale interaction in Large Igneous Provinces

2020· article· en· W3042226245 on OpenAlexaffabout
Frances M. Deegan, Jean H. Bédard, Valentín R. Troll, Keith Dewing, Harri Geiger, Steve E. Grasby, Valeria Misiti, C. Freda

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGeology and Paleoclimatology Research
Canadian institutionsGeological Survey of Canada
Fundersnot available
KeywordsMagmaGeologyOil shaleGeochemistrySillSulfideIgneous rockLarge igneous provinceDikeAnoxic watersEarth scienceMagmatismVolcanoChemistryPaleontologyOceanography

Abstract

fetched live from OpenAlex

Large Igneous Province (LIP) activity is hypothesized to impact global volatile cycles causing climate changes and environmental crises deleterious to the biosphere. Recent work suggests that the potential of LIPs to impact climate is magnified where they intrude organic-rich (i.e. shale-bearing) sedimentary basins. However, the chemical and degassing dynamics of magma-shale interaction are not well understood. Here we present the first experimental simulations of disequilibrium interaction between LIP magma and carbonaceous shale during upper crustal sill intrusions in the Canadian High Arctic LIP (HALIP), the latter of which were co-eval with oceanic anoxic event 1a. Experiments show that magma-shale interaction results in intense syn-magmatic degassing and simultaneous precipitation of sulfide droplets at the ablation interface. Magma-shale interaction on a basin-scale can thus generate substantial amounts of climate-active H-C-S volatiles, while the presence of strongly reducing volatiles may also increase the likelihood of magma to segregate a sulfide melt. These findings have fundamental consequences for our understanding of both large-scale Earth outgassing and metal prospectivity in sediment-hosted LIPs.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.254
Threshold uncertainty score0.505

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 studies0.0010.001
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.019
GPT teacher head0.240
Teacher spread0.221 · 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

Citations0
Published2020
Admission routes2
Has abstractyes

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