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Record W4296335838 · doi:10.1029/2022gl099808

The Influence of Transport Stage on Preserved Fluvial Cross Strata

2022· article· en· W4296335838 on OpenAlexaff
Debsmita Das, Vamsi Ganti, R. W. Bradley, Jeremy G. Venditti, Arnold Jan H. Reesink, Daniel R. Parsons

Bibliographic record

VenueGeophysical Research Letters · 2022
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGeological formations and processes
Canadian institutionsSimon Fraser UniversityBC Hydro (Canada)
FundersAmerican Chemical SocietyUniversity of CaliforniaNational Science Foundation
KeywordsBedformFluvialGeologyStage (stratigraphy)Sediment transportFlow (mathematics)SedimentGeomorphologyTrough (economics)Sedimentary depositional environmentPaleontologyGeometryMathematics

Abstract

fetched live from OpenAlex

Abstract Fluvial cross strata are depositional products of bedform migration that record formative flow and sediment transport conditions on planetary bodies. Bedform evolution varies with transport stage even under constant flow depths, but our understanding of how prevailing sediment transport conditions affect preserved cross strata is limited. Here, we analyzed experimental bedform evolution and preserved set thickness spanning threshold‐of‐motion to suspension‐dominated transport conditions at multiple equilibrium flow depths. Results show that bedform trough depth and mean preserved set thickness have a parabolic dependence on transport stage, with maximum values observed at intermediate transport stages. Our results indicate that transport stage is a key control on the flow‐depth‐normalized set thickness but set thickness is a poor indicator of flow depth. Thus, the dependence of bedform dimensions on transport stage should be considered in paleohydraulic reconstruction, and the analysis of set thickness may aid in the estimation of ancient fluvial sediment flux.

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)0.000
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.687
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.047
GPT teacher head0.309
Teacher spread0.262 · 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

Citations16
Published2022
Admission routes1
Has abstractyes

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