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Record W3043489722 · doi:10.1130/g47460.1

Eolian megaripple stripes

2020· article· en· W3043489722 on OpenAlexaff
Tyler Gough, Chris H. Hugenholtz, Thomas E. Barchyn

Bibliographic record

VenueGeology · 2020
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGeology and Paleoclimatology Research
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsBedformAeolian processesGeologyGeomorphologyAutocorrelationSedimentSediment transport

Abstract

fetched live from OpenAlex

Abstract We present observations, measurements, and modeling of an enigmatic eolian bedform pattern of cross-wind alternating, wind-parallel corridors of megaripples and smaller bedforms (“megaripple stripes”). Megaripple corridors have taller bedforms, longer wavelengths, and coarser surface sediment than intervening smaller bedform corridors. We document examples from Earth (Argentina, Namibia, United States, Iran, Peru, and China) and Mars. Using a reduced complexity model, we show that megaripples and megaripple stripes initiate under the influence of two eolian transport length scales: long-hop saltons and short-hop reptons. The self-organizing stripe pattern manifests in a narrow range of repton concentrations and develops into more typical megaripples as the surface repton concentration increases. We show that the three-dimensional topography of simulated megaripple stripes closely resembles natural megaripple stripes at Oceano Dunes, California, USA. By tracking repton surface concentration and spatial autocorrelation during simulations, we show that the striped pattern initiates from local repton concentrations of sufficient size to serve as megaripple nuclei that seed the striped pattern. Results suggest that megaripple stripes may have a simple and robust formation mechanism.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.099
Threshold uncertainty score0.994

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)0.0200.007

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 head0.029
GPT teacher head0.230
Teacher spread0.201 · 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

Citations7
Published2020
Admission routes1
Has abstractyes

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