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Record W4294718946 · doi:10.1016/j.xinn.2022.100309

Evolving mantle convection from bottom up to top down

2022· article· en· W4294718946 on OpenAlexaff
Ross N. Mitchell, Michael Brown, Thomas Gernon, Christopher J. Spencer

Bibliographic record

VenueThe Innovation · 2022
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicHigh-pressure geophysics and materials
Canadian institutionsQueen's University
FundersNational Natural Science Foundation of ChinaChinese Academy of SciencesNational Science Foundation
KeywordsGeologyMantle convectionMantle (geology)ConvectionGeophysicsMeteorologySeismologyLithosphereGeography

Abstract

fetched live from OpenAlex

When it comes to convection, what goes up must come down. Or is it, what goes down must come up? The truth is it depends. Although convection must be mass balanced, there is no reason that it must be force balanced: the positive and negative buoyancy forces driving convection up and down, respectively, do not necessarily need to be balanced. The balance, or imbalance, all depends on the top and bottom boundary layers. Thus, convection in Earth's mantle depends on the temperature differences across the core-mantle boundary below and the lithosphere-asthenosphere boundary above. Convective asymmetry predominated by positive buoyancy, or bottom-up convection, would be driven by plume ascent, whereas if it were predominated by negative buoyancy, or top-down convection, it would be driven by plate subduction. Symmetric convection would balance plume ascent and plate subduction. Is mantle convection on Earth balanced, dominantly top down or bottom up, or time dependent?

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: none
Teacher disagreement score0.003
Threshold uncertainty score0.011

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 studies0.0010.001
Scholarly communication0.0020.002
Open science0.0010.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.001

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 head0.014
GPT teacher head0.215
Teacher spread0.202 · 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

Citations10
Published2022
Admission routes1
Has abstractyes

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