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Record W3183872154 · doi:10.1017/s0016756821000534

Beyond ‘crumple zones’: recent advances, applications and future directions in deformable plate tectonic modelling

2021· article· en· W3183872154 on OpenAlexaff
Alexander L. Peace

Bibliographic record

VenueGeological Magazine · 2021
Typearticle
Languageen
FieldEarth and Planetary Sciences
Topicearthquake and tectonic studies
Canadian institutionsMcMaster University
Fundersnot available
KeywordsKinematicsDeformation (meteorology)Plate tectonicsGeologyIntraplate earthquakeWork (physics)Computer scienceTectonicsMechanical engineeringSeismologyPhysicsClassical mechanicsEngineering

Abstract

fetched live from OpenAlex

Abstract The recent proliferation of deformable plate tectonic modelling techniques has provided a new direction in the study of plate tectonics with substantial implications for our understanding of plate deformation and past kinematics. Such models account for intraplate deformation, yet are highly variable in their inputs, capabilities and applications. The aim of this commentary is to review recent contributions to this topic, and to consider future directions and major omissions. Through this review it is apparent that the current published deformable models can be subdivided into those that as an input either: (1) solely use plate motions to drive deformation, or (2) require stretching or beta factor. Deformable models are resolving some outstanding issues with plate reconstructions, but major simplifications and modelling assumptions remain. Primarily, obtaining model constraints on the spatio-temporal evolution of deformation is an outstanding problem. Deformable plate models likely work best when the kinematics of smaller plates are included. However, questions remain regarding how to define such blocks, and their kinematic histories, whilst some work suggests that inclusion of such entities is negated through quantitative restorations.

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: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.806
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
Bibliometrics0.0000.001
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 head0.016
GPT teacher head0.220
Teacher spread0.204 · 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 designOther design
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

Citations5
Published2021
Admission routes1
Has abstractyes

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