MétaCan
Menu
Back to cohort
Record W3198885582 · doi:10.1130/g49206.1

Active faulting controls bedform development on a deep-water fan

2021· article· en· W3198885582 on OpenAlexafffund
Vittorio Maselli, Aaron Micallef, Alexandre Normandeau, Davide Oppo, David Iacopini, Andrew Green, Zhiyuan Ge

Bibliographic record

VenueGeology · 2021
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGeological formations and processes
Canadian institutionsGeological Survey of CanadaNatural Resources CanadaDalhousie University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsGeologyBedformSeafloor spreadingTurbidity currentCanyonFault (geology)Sedimentary rockGeomorphologySeismologyTectonicsSedimentary basinStructural basinPaleontologySedimentary depositional environmentSedimentSediment transport

Abstract

fetched live from OpenAlex

Abstract Tectonically controlled topography influences deep-water sedimentary systems. Using 3-D seismic reflection data from the Levant Basin, eastern Mediterranean Sea, we investigate the spatial and temporal evolution of bedforms on a deep-water fan cut by an active normal fault. In the footwall, the fan comprises cyclic steps and antidunes along its axial and external portions, respectively, which we interpret to result from the spatial variation in flow velocity due to the loss of confinement at the canyon mouth. Conversely, in the hanging wall, the seafloor is nearly featureless at seismic scale. Numerical modeling of turbidity currents shows that the fault triggers a hydraulic jump that suppresses the flow velocity downstream, which thus explains the lack of visible bedforms basinward. This study shows that the topography generated by active normal faulting controls the downslope evolution of turbidity currents and the associated bedforms and that seafloor geomorphology can be used to evince syn-tectonic deposition.

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 categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.762
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)0.0100.002

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 head0.015
GPT teacher head0.213
Teacher spread0.198 · 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; both teacher heads agree on what is shown here.

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
Published2021
Admission routes2
Has abstractyes

Explore more

Same venueGeologySame topicGeological formations and processesFrench-language works237,207