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Record W4283376291 · doi:10.1007/s00410-022-01922-9

Variation of plagioclase shape with size in intermediate magmas: a window into incipient plagioclase crystallisation

2022· article· en· W4283376291 on OpenAlexaff
Martin Mangler, Madeleine C. S. Humphreys, Fabian B. Wadsworth, Alexander A. Iveson, Michael D. Higgins

Bibliographic record

VenueContributions to Mineralogy and Petrology · 2022
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGeological and Geochemical Analysis
Canadian institutionsUniversité du Québec à Chicoutimi
FundersDurham UniversityNatural Environment Research CouncilSight Research UK
KeywordsPlagioclaseNucleationGeologyCrystal (programming language)CrystallizationMagmaMineralogyCrystal growthVolcanoSupercoolingCrystal habitCrystallographyGeochemistryQuartzChemistryThermodynamicsPhysics

Abstract

fetched live from OpenAlex

Abstract Volcanic rocks commonly display complex textures acquired both in the magma reservoir and during ascent to the surface. While variations in mineral compositions, sizes and number densities are routinely analysed to reconstruct pre-eruptive magmatic histories, crystal shapes are often assumed to be constant, despite experimental evidence for the sensitivity of crystal habit to magmatic conditions. Here, we develop a new program ( ShapeCalc ) to calculate 3D shapes from 2D crystal intersection data and apply it to study variations of crystal shape with size for plagioclase microlites ( l < 100 µm) in intermediate volcanic rocks. The smallest crystals tend to exhibit prismatic 3D shapes, whereas larger crystals ( l > 5–10 µm) show progressively more tabular habits. Crystal growth modelling and experimental constraints indicate that this trend reflects shape evolution during plagioclase growth, with initial growth as prismatic rods and subsequent preferential overgrowth of the intermediate dimension to form tabular shapes. Because overgrowth of very small crystals can strongly affect the external morphology, plagioclase microlite shapes are dependent on the available growth volume per crystal, which decreases during decompression-driven crystallisation as crystal number density increases. Our proposed growth model suggests that the range of crystal shapes developed in a magma is controlled by the temporal evolution of undercooling and total crystal numbers, i.e., distinct cooling/decompression paths. For example, in cases of slow to moderate magma ascent rates and quasi-continuous nucleation, early-formed crystals grow larger and develop tabular shapes, whereas late-stage nucleation produces smaller, prismatic crystals. In contrast, rapid magma ascent may suppress nucleation entirely or, if stalled at shallow depth, may produce a single nucleation burst associated with tabular crystal shapes. Such variation in crystal shapes have diagnostic value and are also an important factor to consider when constructing CSDs and models involving magma rheology.

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)0.001
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 score0.003
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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 head0.004
GPT teacher head0.198
Teacher spread0.194 · 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

Citations35
Published2022
Admission routes1
Has abstractyes

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