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Record W1999920376 · doi:10.1134/s0016852110060075

Impact of surface processes on the growth of orogenic wedges: Insights from analog models and case studies

2010· article· en· W1999920376 on OpenAlexaff
Jacques Malavieille, Elena Konstantinovskaya

Bibliographic record

VenueGeotectonics · 2010
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGeological and Geochemical Analysis
Canadian institutionsInstitut National de la Recherche Scientifique
Fundersnot available
KeywordsGeologyTectonicsForeland basinStructural geologyErosionWedge (geometry)ThrustKinematicsGeomorphologyGeophysicsPaleontologyGeometry

Abstract

fetched live from OpenAlex

Interaction between surface processes and deep tectonic processes plays a key role in the structural evolution, kinematics and exhumation of rocks in orogenic wedges. The deformation patterns observed in analogue models applied to natural cases of present active or ancient mountain belts reflect several first order processes that result of these interactions. Internal strain partitioning due to mechanical behaviour of a thrust wedge has a strong impact on the vertical component of displacement of tectonic units that in return favour erosion in domains of important uplift. Such strain partitioning is first controlled by tectonic processes, but surface processes exert a strong feed back on wedge dynamics. Indeed, material transfer in thrust wedges not only depends on its internal dynamics, it is also influenced by climate controlled surface processes involving erosion and sedimentation. Effects of erosion are multiple: they allow long term localization of deformed domains, they favour important exhumation above areas of deep underplating and combined with sedimentation in the foreland they contribute to maintain the wedge in a critical state for long time periods. The simple models illustrate well how mountain belts structure, kinematics of tectonic units and exhumation are determined by these complex interactions.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.535
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.027
GPT teacher head0.238
Teacher spread0.211 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations32
Published2010
Admission routes1
Has abstractyes

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