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Record W1549979703 · doi:10.1002/esp.3301

Assessing aeolian beach‐surface dynamics using a remote sensing approach

2012· article· en· W1549979703 on OpenAlexaffabout
Irene Delgado‐Fernández, Robin Davidson‐Arnott, Bernard O. Bauer, Ian J. Walker, Jeff Ollerhead, Hosahng Rhew

Bibliographic record

VenueEarth Surface Processes and Landforms · 2012
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicAeolian processes and effects
Canadian institutionsMount Allison UniversityUniversity of British Columbia, Okanagan CampusUniversity of British ColumbiaUniversity of VictoriaUniversity of Guelph
Fundersnot available
KeywordsForeduneBermAeolian processesFetchGeologyWater contentHydrology (agriculture)Beach morphodynamicsTransectShoreMoistureRemote sensingGeomorphologyEnvironmental scienceSedimentSediment transportMeteorologyOceanographyGeographyGeotechnical engineering

Abstract

fetched live from OpenAlex

ABSTRACT A remote sensing technique for assessing beach surface moisture was used to provide insight into beach‐surface evolution during an aeolian event. An experiment was carried out on 21 October 2007 at Greenwich Dunes, Prince Edward Island National Park, Canada, during which cameras were mounted on a mast on the foredune crest at a height of about 14 m above the beach. Maps of beach surface moisture were created based on a calibrated relationship between surface brightness from the photographs and surface moisture content measured in situ at points spaced every 2.5 m along a transect using a Delta‐T moisture probe. A time sequence of maps of surface moisture content captured beach surface evolution through the transport event at a spatial and temporal resolution that would be difficult to achieve with other sampling techniques such as impedance probes. Erosion of the foreshore and berm crest resulted in an increase in surface moisture content in these areas as the wetter underlying sediments were exposed. Flow expansion downwind of the berm crest led to sand deposition and a consequent decrease in surface moisture content. Remote sensing systems such as the one presented here allow observations of the combined evolution of beach surface moisture, shoreline position, and fetch distances during short‐term experiments and hence provide a comprehensive rendering of sediment erosion and transport processes. Copyright © 2012 John Wiley & Sons, Ltd.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.895
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
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.002
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.023
GPT teacher head0.244
Teacher spread0.221 · 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 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

Citations26
Published2012
Admission routes2
Has abstractyes

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