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Record W1955979307 · doi:10.1029/2007gc001834

Integrated geophysical‐petrological modeling of the lithosphere and sublithospheric upper mantle: Methodology and applications

2008· article· en· W1955979307 on OpenAlexafffund
Juan Carlos Afonso, Manel Fernández, G. Ranalli, William L. Griffin, J. A. D. Connolly

Bibliographic record

VenueGeochemistry Geophysics Geosystems · 2008
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicHigh-pressure geophysics and materials
Canadian institutionsCarleton University
FundersNatural Sciences and Engineering Research Council of CanadaEuropean Social FundMacquarie University
KeywordsGeologyLithosphereGeophysicsMantle (geology)ObservableSeismologyTectonicsPhysics

Abstract

fetched live from OpenAlex

A combined geophysical‐petrological methodology to study the thermal, compositional, density, and seismological structure of lithospheric/sublithospheric domains is presented. A new finite‐element code (LitMod) is used to produce 2‐D forward models from the surface to the 410‐km discontinuity. The code combines data from petrology, mineral physics, and geophysical observables within a self‐consistent framework. The final result is a lithospheric/sublithospheric model that simultaneously fits all geophysical observables and consequently reduces the uncertainties associated with the modeling of these observables alone or in pairs, as is commonly done. The method is illustrated by applying it to both oceanic and continental domains. We show that anelastic attenuation and uncertainties in seismic data make it unfeasible to identify compositional variations in the lithospheric mantle from seismic studies only. In the case of oceanic lithosphere, plates with thermal thicknesses of 105 ± 5 km satisfy geophysical and petrological constraints. We find that Vp are more sensitive to phase transitions than Vs, particularly in the case of the spinel‐garnet transition. A low‐velocity zone with absolute velocities and gradients comparable to those observed below ocean basins is an invariable output of our oceanic models, even when no melt effects are included. In the case of the Archean subcontinental lithospheric mantle, we show that “typical” depleted compositions (and their spatial distribution) previously thought to be representative of these mantle sections are compatible neither with geophysical nor with petrological data. A cratonic keel model consisting of (1) strongly depleted material (i.e., dunitic/harzburgitic) in the first 100–160 km depth and (2) less depleted (approximately isopycnic) lower section extending down to 220–300 km depth is necessary to satisfy elevation, geoid, SHF, seismic velocities, and petrological constraints. This highly depleted (viscous) upper layer, and its chemical isolation, may play a key role in the longevity and stability of cratons.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.004
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0020.001
Research integrity0.0000.001
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 head0.025
GPT teacher head0.211
Teacher spread0.187 · 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 designSimulation or modeling
Domainnot available
GenreMethods

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

Citations289
Published2008
Admission routes2
Has abstractyes

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