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Record W2346900238 · doi:10.1002/2016gl068562

Inherited structure and coupled crust‐mantle lithosphere evolution: Numerical models of Central Australia

2016· article· en· W2346900238 on OpenAlexafffund
Philip J. Heron, R. N. Pysklywec

Bibliographic record

VenueGeophysical Research Letters · 2016
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGeological and Geochemical Analysis
Canadian institutionsUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of CanadaCanada Foundation for Innovation
KeywordsGeologyIntraplate earthquakeLithosphereCrustTectonicsMantle (geology)Plate tectonicsGlacial periodContext (archaeology)GeophysicsEarth sciencePaleontology

Abstract

fetched live from OpenAlex

Abstract Continents have a rich tectonic history that have left lasting crustal impressions. In analyzing Central Australian intraplate orogenesis, complex continental features make it difficult to identify the controls of inherited structure. Here the tectonics of two types of inherited structures (e.g., a thermally enhanced or a rheologically strengthened region) are compared in numerical simulations of continental compression with and without “glacial buzzsaw” erosion. We find that although both inherited structures produce deformation in the upper crust that is confined to areas where material contrasts, patterns of deformation in the deep lithosphere differ significantly. Furthermore, our models infer that glacial buzzsaw erosion has little impact at depth. This tectonic isolation of the mantle lithosphere from glacial processes may further assist in the identification of a controlling inherited structure in intraplate orogenesis. Our models are interpreted in the context of Central Australian tectonics (specifically the Petermann and Alice Springs orogenies).

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)0.002
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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.205
Threshold uncertainty score0.409

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0020.001
Research integrity0.0020.001
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 head0.037
GPT teacher head0.266
Teacher spread0.229 · 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

Citations14
Published2016
Admission routes2
Has abstractyes

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