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Record W3041575128 · doi:10.1038/s41467-020-17267-5

Large-scale mass wasting in the western Indian Ocean constrains onset of East African rifting

2020· article· en· W3041575128 on OpenAlexafffund
Vittorio Maselli, David Iacopini, C. J. Ebinger, Sugandha Tewari, H. de Haas, Bridget S. Wade, Paul N. Pearson, M. Francis, Arjan van Vliet, Bill Richards, Dick Kroon

Bibliographic record

VenueNature Communications · 2020
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGeological formations and processes
Canadian institutionsDalhousie University
FundersNatural Sciences and Engineering Research Council of CanadaCanada First Research Excellence FundOcean Frontier InstituteResearch Councils UKNederlandse Organisatie voor Wetenschappelijk OnderzoekSight Research UKUnited States Science Support ProgramEuropean Social FundNatural Environment Research CouncilShellNational Science Foundation
KeywordsRiftGeologyLandslideMass wastingSubmarine landslideEast African RiftPaleontologyCratonContinental marginSubmarine pipelineTectonicsSeismologySeafloor spreadingOceanography

Abstract

fetched live from OpenAlex

Faulting and earthquakes occur extensively along the flanks of the East African Rift System, including an offshore branch in the western Indian Ocean, resulting in remobilization of sediment in the form of landslides. To date, constraints on the occurrence of submarine landslides at margin scale are lacking, leaving unanswered a link between rifting and slope instability. Here, we show the first overview of landslide deposits in the post-Eocene stratigraphy of the Tanzania margin and we present the discovery of one of the biggest landslides on Earth: the Mafia mega-slide. The emplacement of multiple landslides, including the Mafia mega-slide, during the early-mid Miocene is coeval with cratonic rifting in Tanzania, indicating that plateau uplift and rifting in East Africa triggered large and potentially tsunamigenic landslides likely through earthquake activity and enhanced sediment supply. This study is a first step to evaluate the risk associated with submarine landslides in the region.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.082
Threshold uncertainty score0.293

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 science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.031
GPT teacher head0.251
Teacher spread0.220 · 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

Citations28
Published2020
Admission routes2
Has abstractyes

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