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Record W4289131819 · doi:10.1111/cag.12792

Textbook dune: Is there a representative and scale‐invariant beach‐dune profile?

2022· article· en· W4289131819 on OpenAlexafffundvenue
Chris Houser, Alex Smith, Brianna Lunardi, Elizabeth George, Jacob Lehner

Bibliographic record

VenueCanadian Geographies / Géographies canadiennes · 2022
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicAeolian processes and effects
Canadian institutionsUniversity of Windsor
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsSediment transportScale (ratio)GeologyAeolian processesSand dune stabilizationPlageScale invarianceOceanographyGeographySedimentGeomorphologyCartographyShoreMathematics

Abstract

fetched live from OpenAlex

The Coastal Dune Model, used to examine controls on dune development and evolution, includes negative feedback between the wind and the evolving topography that results in a scale‐invariant beach‐dune profile. While a limited number of reference profiles appear to support the Coastal Dune Model assumption of scale‐invariance, previously published studies and conceptual models suggest that the beach‐dune profile is not scale‐invariant. Using a combination of journal articles, textbooks, and remotely sensed data from Prince Edward Island, Washington state, and Texas, this short communication finds that there is no evidence for a scale‐invariant beach‐dune profile within and between field sites. Variability in the beach‐dune profile is due to site‐specific and scale‐dependent controls on the beach and the dune that are often highlighted in the conceptual models that authors use to depict the controls on sediment supply and transport from the beach to the dune. Results highlight a need to critically evaluate the assumptions and applications of models such as the Coastal Dune Model, and to explore how and when sites can be considered representative and in support of model results .

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 categoriesMeta-epidemiology (narrow), Science and technology studies, Insufficient 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 score0.163
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.004
Science and technology studies0.0020.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0050.000

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 head0.007
GPT teacher head0.184
Teacher spread0.178 · 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

Citations4
Published2022
Admission routes3
Has abstractyes

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