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Record W2795655823 · doi:10.1002/2018jf004629

Modeling the Mechanisms Behind Yardang Evolution

2018· article· en· W2795655823 on OpenAlexaff
Thomas E. Barchyn

Bibliographic record

VenueJournal of Geophysical Research Earth Surface · 2018
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicAeolian processes and effects
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsAeolian processesSuiteAridGeologyField (mathematics)ErosionEarth scienceWater erosionWork (physics)ArchaeologyGeographyGeomorphologyPaleontologyEngineeringMathematics

Abstract

fetched live from OpenAlex

Abstract New work by Pelletier et al. (2018, https://doi.org/10.1002/2017JF004461 ) and Pelletier (2018, https://doi.org/10.1002/2017JF004462 ) quantitatively explains how arid landscapes can be sculpted into regular “teardrop”‐shaped hills known as yardangs. A diversity of theories have been proposed to explain these perplexing hills. Pelletier (2018) helpfully clarifies that the most likely suite of mechanisms involves an interplay between wind‐ and water‐driven erosion. Using detailed field measurements from Ocotillo Wells, California, a model is developed that explains how yardangs achieve their characteristic form and spacing. Although couched around the iconic yardang, the work contributes a general and robust template for understanding landscapes where both wind and water erosion are present.

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)0.003
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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.211
Threshold uncertainty score0.930

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
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 science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.001

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 head0.309
Teacher spread0.267 · 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 designSimulation or modeling
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

Citations10
Published2018
Admission routes1
Has abstractyes

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