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Record W2874971891 · doi:10.1038/s41598-018-29105-2

Growth of mountain belts in central Asia triggers a new collision zone in central India

2018· article· en· W2874971891 on OpenAlexfundno aff
Iván Koulakov, Taras Gerya, B. K. Rastogi, Andrey Jakovlev, Irina Medved, J. R. Kayal, Nassir Al‐Arifi

Bibliographic record

VenueScientific Reports · 2018
Typearticle
Languageen
FieldEarth and Planetary Sciences
Topicearthquake and tectonic studies
Canadian institutionsnot available
FundersRussian Science FoundationKing Saud UniversityRussian Foundation for Basic ResearchInstitute of Population and Public HealthDepartment of Science and Technology, Ministry of Science and Technology, India
KeywordsCollision zoneGeologyLithosphereCentral asiaLineamentContinental collisionCollisionSeismologyInduced seismicityPlate tectonicsEurasian PlateSouth asiaTectonicsGeographySubductionPhysical geographyAncient historyHistory

Abstract

fetched live from OpenAlex

Several unusual strong earthquakes occurred in central India along the Narmada-Son Lineament (NSL) zone, far from active plate boundaries. To understand the role of collisional processes in the origin of this seismicity, we develop a numerical thermomechanical model of shortening between the Indian Plate and Asia. We show that at the final stage of collision, the shortening rate of the high mountain areas slows. The continuing convergence of India and Asia triggers the initiation of a new collision zone in continental part of India. Various geological and geophysical observations indicate that the NSL is a weakest zone with northward thrusting of the thinner central Indian lithosphere underneath the thicker northern part of the Indian Plate. We hypothesize that the NSL was reactivated during the final stage of the India Asia convergence and it will possibly form a new mountain belt within the Indian continent.

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)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.022
Threshold uncertainty score0.043

Distilled classifier scores by category (both heads)

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 communication0.0010.000
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 head0.012
GPT teacher head0.220
Teacher spread0.208 · 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 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

Citations25
Published2018
Admission routes1
Has abstractyes

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