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Record W4281396306 · doi:10.1029/2021gl097389

Detailed Seafloor Imagery of Turbidity Current Bedforms Reveals New Insight Into Fine‐Scale Near‐Bed Processes

2022· article· en· W4281396306 on OpenAlexafffund
Alexandre Normandeau, Patrick Lajeunesse, Jean‐François Ghienne, Pierre Dietrich

Bibliographic record

VenueGeophysical Research Letters · 2022
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGeological formations and processes
Canadian institutionsUniversité LavalGeological Survey of CanadaNatural Resources Canada
FundersNatural Sciences and Engineering Research Council of CanadaMinistère de l'Économie, de l’Innovation et des Exportations du QuébecUniversité Laval
KeywordsTurbidity currentBedformTurbiditeGeologyBathymetryCurrent (fluid)Seafloor spreadingGeomorphologyOutcropErosionFlumeFlow (mathematics)Sedimentary depositional environmentOceanographySediment transportSedimentGeometryStructural basin

Abstract

fetched live from OpenAlex

Abstract High‐resolution imagery of the morphological character of modern active turbidite systems are critical for understanding the complexity of turbidity current processes occurring on the seafloor. Here, we describe a 30 cm‐resolution autonomous underwater vehicle repeat bathymetric dataset that allows to significantly increase the geomorphological detail of crescentic bedforms in turbidite systems. This repeat imagery shows the erosion produced by dense basal layers at the base of turbidity currents, the inception of plunge pools, and the controls that hydraulic jump troughs have on subsequent flow path of weaker turbidity currents. Transverse small‐scale scours located in cyclic step troughs suggest that weak flows follow local relief, therefore explaining the wide variability of turbidity current flow indicators observed in outcrops. This imagery demonstrates that turbidite systems are formed by the superimposition of erosion surfaces and depositional patterns recording contrasted flow behavior and provides new views on turbidity current behavior in natural environments.

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)0.001
metaresearch head score (Gemma)0.001
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 score0.321
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.042
GPT teacher head0.296
Teacher spread0.254 · 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

Citations14
Published2022
Admission routes2
Has abstractyes

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