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Record W4389430823 · doi:10.1130/g51439.1

Magma-carbonate interactions drive CO2 production and metal enrichment in shallow dikes and sills at volcanic arcs

2023· article· en· W4389430823 on OpenAlexaffabout
Rebecca Morris, Dante Canil, Jody Spence

Bibliographic record

VenueGeology · 2023
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGeological and Geochemical Analysis
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsSillGeologyDikeVolcanoGeochemistryCarbonatePetrologyMagmaPlutonBasaltPaleontologyTectonics

Abstract

fetched live from OpenAlex

Abstract The contribution of CO2 from crustal carbonates into arc magmas is debated, as is its role in the long-term C cycle. To better understand the contributions and mechanisms that drive CO2 production in arc magmas, we examined in detail basaltic dike and sill contacts with carbonate in the Jurassic Bonanza arc on Vancouver Island, Canada. We discovered discrete boundary melts that formed along dike and sill margins in contact with limestone, which display unique Ca, U, and Sr enrichments, Si depletion, and 87Sr/86Sr that approaches host limestone values (~0.708). Binary mixing modeling indicates ~20%–25% limestone assimilation into basalt formed the boundary melts. Contrasting viscosities between boundary and interior melts hinder mixing and chemical homogenization but appear to promote uphill diffusion and metal enrichment within systems that cool in minutes to days. While shallow dikes and sills may be volumetrically minor in an arc magma system, the open flow of magma and large surface area in channels greatly enhances magma-carbonate interactions, and ultimately CO2 production, likely over that of more common and voluminous plutons.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.070
Threshold uncertainty score0.999

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 communication0.0000.000
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 head0.011
GPT teacher head0.208
Teacher spread0.197 · 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

Citations5
Published2023
Admission routes2
Has abstractyes

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