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Record W2464051932

The persistence of large-scale blowouts in largely vegetated coastal dune fields

2016· article· en· W2464051932 on OpenAlexaboutno aff
Irene Delgado‐Fernández, Thomas Smyth, Derek Jackson, Robin Davidson‐Arnott, Alexander B. Smith

Bibliographic record

VenueEdge Hill University Research Information Repository (Edge Hill University) · 2016
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicAeolian processes and effects
Canadian institutionsnot available
Fundersnot available
KeywordsSand dune stabilizationGeologyVegetation (pathology)Physical geographyHydrology (agriculture)GeomorphologyGeography
DOInot available

Abstract

fetched live from OpenAlex

Coastal dunes move through natural phases of stability and instability during their evolution, displaying various temporal and spatial patterns across the dune field. Recent observations, however, have shown exceptionally rapid rates of stability through increased vegetative growth. This progressive vegetation colonisation and consequent loss of bare sand on coastal dune systems has been noted worldwide. Percentage reductions in bare sand of as much as 80% within just a few decades can been seen in examples from South Africa, Canada and Brazil as well as coastal dune sites across NW Europe. Despite these dramatic trends towards dune stabilisation, it is not uncommon to find particular examples of large-scale active blowouts and parabolic dunes within largely vegetated coastal dunes. While turbulence and airflow dynamics within features such as blowouts and other dune forms has been studied in detail within recent years, there is a lack of knowledge about what maintains dune mobility at these specific points in otherwise largely stabilized dune fields. This work explores the particular example of the ‘Devil’s Hole’ blowout, Sefton Dunes, NW England. Approximately 300 m long by 100 m wide, its basin is below the water-table which leads to frequent flooding. Sefton Dunes in general have seen a dramatic loss of bare sand since the 1940s. However, and coinciding with this period of dune stabilisation, the ‘Devil’s Hole’ has not only remained active but also grown in size at a rate of 4.5 m year-1 along its main axis. An exploration of factors controlling the maintenance of open bare sand areas at this particular location is examined using a variety of techniques including Computational Fluid Dynamics (CFD) airflow modelling and in situ empirical measurements of (short-term experiments) of wind turbulence and sand transport. Field measurements of wind parameters and transport processes were collected over a 2 week period during October 2015. Twenty three 3D ultrasonic anemometers were deployed at 0.5 m elevations over a grid covering sections of the blowout walls, deflation basin and depositional lobe. A number of high resolution sand traps and wenglor sensors were co-located with anemometers in the walls and basin, and a terrestrial laser scanner was used to collect high-resolution topographic data both before and after the strongest transport event recorded during the study period. Preliminary results indicate significant transport differences in operation at each of the two blow out walls as well as complex interactions between turbulence, superficial moisture content and up-wind sediment sources. This study represents a comprehensive examination of both wind and sediment flux patterns at high spatial and temporal resolution inside a large trough blowout feature; and reveals insights into why such systems are maintained as erosional features for long time periods.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.025
Threshold uncertainty score0.050

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.016
GPT teacher head0.209
Teacher spread0.192 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations0
Published2016
Admission routes1
Has abstractyes

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