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Record W2884796599 · doi:10.1144/sp483.2

The South Tibetan Detachment System: history, advances, definition and future directions

2018· article· en· W2884796599 on OpenAlexafffund
Dawn A. Kellett, John M. Cottle, Kyle P. Larson

Bibliographic record

VenueGeological Society London Special Publications · 2018
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGeological and Geochemical Analysis
Canadian institutionsOkanagan University CollegeUniversity of British Columbia, Okanagan CampusUniversity of British ColumbiaGeological Survey of CanadaNatural Resources Canada
FundersNatural Sciences and Engineering Research Council of CanadaDirectorate for Geosciences
KeywordsGeologyTectonicsDetachment faultCrustTRACE (psycholinguistics)Shear zoneFault (geology)Boundary (topology)PaleontologySeismology

Abstract

fetched live from OpenAlex

Abstract Recognition and subsequent study of the syn-convergent low-angle normal faults and shear zones – the South Tibetan Detachment System (STDS) – that form the upper boundary of the Himalayan mid-crust fundamentally changed views of how the Himalayan orogenic system developed. This paper reviews the past four decades of discovery and major advances in our understanding of the detachment system. Significantly conflicting maps of the fault trace, as well as proposed extensions of the detachment system up to hundreds of kilometres both up and down dip of the main fault trace, call for a unifying definition of the detachment system based on structural criteria. The different proposed models for the formation of the STDS during tectonic evolution of the Himalayan orogen are compared. Finally, critical outstanding questions about the origin, extent and character of the detachment system are identified and point to future directions for research.

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)0.001
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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.010
Threshold uncertainty score0.021

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.003
Science and technology studies0.0000.002
Scholarly communication0.0020.002
Open science0.0010.001
Research integrity0.0010.001
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 head0.197
Teacher spread0.181 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations113
Published2018
Admission routes2
Has abstractyes

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