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Record W7117935173 · doi:10.5382/econgeo.5195

Microplate Solutions to Crustal Growth and Metal Endowment in Modern Back-Arc Basins and Ancient Greenstone Belts

2025· article· en· W7117935173 on OpenAlexaffabout
Mark D. Hannington, Alan Baxter, Erin Bethell, Christopher Galley, Marc Fassbender, Margaret Stewart, P Mercier-Langevin, Anna Krätschell, Sven Petersen, Philipp A. Brandl

Bibliographic record

VenueEconomic Geology · 2025
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGeological and Geochemical Analysis
Canadian institutionsAgnico Eagle (Canada)Geological Survey of CanadaMount Royal UniversityCarleton University
Fundersnot available
KeywordsGreenstone beltArcheanCrustRiftHydrothermal circulationPlate tectonicsTectonicsMantle (geology)

Abstract

fetched live from OpenAlex

Abstract Ore formation throughout Earth’s history has tracked major pulses of crustal growth. The spectacular endowment of some greenstone belts, in particular, has been linked to high heat flow and extensive Archean rifting. Three aspects were likely important: (1) greater numbers of plates required to dissipate the heat; (2) abundant crustal-scale transcurrent faults to accommodate plate growth; and (3) increased hydrothermal convection to cool the crust at the plate boundaries. Because the plates were smaller and more numerous than today, the total ridge length was greater, thus allowing for more efficient cooling of the newly formed crust. Mantle upwelling and rifting between microplate domains focused melts and fluids into well-mineralized corridors. This tectonic style is observed today at the Indo-Australian margin, providing clues to the crustal architecture of some well-endowed Archean terranes, such as the Abitibi greenstone belt in the Superior province of Canada. Hot, thickened oceanic crust, like that of the modern Lau basin and North Fiji basin, has strong similarities to mineral-rich greenstone belts like the Abitibi in terms of structure, kinematics, and magmatic evolution. The majority of this crust formed during a basin-wide microplate “breakout” that occurred in response to the collision of Australia with the Ontong Java and Melanesian Border plateaus in the Late Miocene. Today, the back-arc basins contain some of the fastest growing crust on Earth and an extraordinary concentration of magmatic and hydrothermal activity. In the northern Lau basin, at least seven distinct microplates formed within the last 5 m.y., with crustal growth partitioned across numerous simultaneously active plate boundaries in a complex microplate mosaic. The plates are bound by active spreading centers, ridges, and shear zones that are continuously deforming in response to plate rotation. Basin opening is dominated by many short, slow-spreading segments between large-scale transcurrent fault zones, with a combined strike length of spreading centers greater than in any other back-arc basin in the western Pacific. Seismic sections to depths of at least 20 km show that the plate boundaries are broad zones of deformation characterized by overlapping spreading centers, ridge jumps, and extensional transforms. Increased crustal permeability occurs where multiple spreading centers intersect (i.e., at triple junctions) with enhanced magmatic and hydrothermal activity at the plate boundaries. Seismic velocities and volcanic geochemistry also show large variations in crustal composition between the plates, indicating that the back-arc region is far more complex than supposed in earlier models. We suggest crustal growth and mineral endowment in some greenstone belts were similarly regulated by microplate formation. Because the microplates behave independently, often at great distances from the nearest subduction zone, their formation is akin to autochthonous growth in the Archean when subduction-zone processes were either absent or in their infancy. Compelling evidence of this architecture is now being revealed in the Abitibi greenstone belt by modeling of the Archean Moho topography.

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 score0.128
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 head0.010
GPT teacher head0.188
Teacher spread0.178 · 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

Citations0
Published2025
Admission routes2
Has abstractyes

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