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Slab Remnant Recycling and Mantle-Wide Convection: A Separation of Time Scales

2020· article· en· W3083420293 on OpenAlexaff
Gary T. Jarvis

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicHigh-pressure geophysics and materials
Canadian institutionsYork University
Fundersnot available
KeywordsMantle (geology)Mantle convectionGeologySlabMantle wedgeGeophysicsConvectionMechanicsSubductionPhysicsTectonicsSeismology

Abstract

fetched live from OpenAlex

Two dimensional numerical models of mantle convection in a cylindrical shell provide a possible geodynamic explanation for cold patches in the mantle below India and Mongolia as detected by seismic tomography. We investigate the influence of very high viscosities at mid-mantle and lower-mantle depths, as proposed by Mitrovica and Forte (2004) and Steinberger and Calderwood (2006), on mantle convective flow. Models are considered with and without mineral phase transitions. Our viscosity profiles are depth dependent with deep mantle viscosities increasing to values of 300 times the viscosity of the upper mantle, and then decreasing dramatically on approaching the core-mantle boundary. The decrease of viscosity near the CMB mobilizes the overall mantle-wide flow despite very high mid-mantle viscosities. However, cold detached slabs sinking below continental collisions become captured by the high viscosity interior and circulate slowly for times exceeding 200 Myr. The separation of time scales for mantle-wide flow vs slab circulation, is a consequence of the high viscosity of the mid-mantle.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.009

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 studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.013
GPT teacher head0.214
Teacher spread0.200 · 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
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 routes1
Has abstractyes

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