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Record W2616285995 · doi:10.1002/2017tc004496

Cooling, exhumation, and kinematics of the Kanchenjunga Himal, far east Nepal

2017· article· en· W2616285995 on OpenAlexafffund
Kyle P. Larson, Alfredo Camacho, John M. Cottle, Isabelle Coutand, Heather Marie Buckingham, Tyler Ambrose, Santa Man

Bibliographic record

VenueTectonics · 2017
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGeological and Geochemical Analysis
Canadian institutionsDalhousie UniversityUniversity of ManitobaOkanagan University CollegeUniversity of British Columbia, Okanagan CampusUniversity of British Columbia
FundersNational Science Foundation of Sri LankaNatural Sciences and Engineering Research Council of CanadaDalhousie UniversityCanada Foundation for InnovationNova Scotia Research Innovation TrustNational Science Foundation
KeywordsGeologyZirconMonaziteFission track datingGeochemistryTectonicsMetamorphic rockSeismologyPaleontology

Abstract

fetched live from OpenAlex

Abstract New single crystal 40 Ar/ 39 Ar and apatite fission track ages from the Kanchenjunga region of far east Nepal yield insight into the timing of assembly of the Himalayan midcrust and the mechanisms that controlled its exhumation. The 40 Ar/ 39 Ar data are compared with new U(Th)/Pb zircon and monazite intrusive crystallization ages and existing metamorphic monazite ages from across the study area to test for internal consistency and potential excess Ar contributions. This new data set, which significantly enhances the density and spatial coverage available from the region, shows that inferred thrust‐sense discontinuities within the now‐exhumed former midcrustal rocks exposed therein must have ceased motion by ~12 Ma. Furthermore, the spatial distribution of ages across the Kanchenjunga region, older ages (~12–16 Ma) to the south and north and younger ages (~8 Ma) in the middle portion of the transect, is compatible with simulations of tectonic‐enhanced exhumation above a developing duplex system in nearby Bhutan.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.027
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
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.019
GPT teacher head0.210
Teacher spread0.191 · 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 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

Citations21
Published2017
Admission routes2
Has abstractyes

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