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Record W4389358221 · doi:10.1130/abs/2023am-394952

MORPHODYNAMIC EVOLUTION OF BOGUE INLET, NC, USA: AN ANNUAL- TO DECADAL-SCALE GEOPHYSICAL, HYDRODYNAMIC, SEDIMENTOLOGICAL, AND MICROFOSSIL ANALYSIS

2023· article· en· W4389358221 on OpenAlexaff
Cody R. Brown, David J. Mallinson, Stephen J. Culver, Ryan P. Mulligan, Stuart Pearson

Bibliographic record

VenueAbstracts with programs - Geological Society of America · 2023
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGeological formations and processes
Canadian institutionsQueen's University
Fundersnot available
KeywordsGeologyInletScale (ratio)GeophysicsOceanographyGeographyCartography

Abstract

fetched live from OpenAlex

Tidal inlets are the most dynamic and complex sedimentary environments in the coastal zone. The morphologic and hydrodynamic processes acting on the ebb-tidal and flood-tidal deltas aided by the tidal prism allow the transport of sand throughout the complex to drive evolution in correlation with ebb-tidal delta volume. By better understanding these morphodynamic relationships in response to the increase in magnitude and frequency of storms, we can understand the future of coastal siliciclastic systems. This study focused on Bogue Inlet, located on a sediment-starved cuspate embayment, Onslow Bay, on the southeastern coast of North Carolina. The study used a multifaceted approach implementing geospatial, geophysical, sedimentological, micropaleontological, hydrodynamic modeling and a novel stratigraphic model methodology. Geospatial data show that this micro-tidal inlet exhibited migration to the northeast. Furthermore, hydrodynamic and geospatial data suggest the migration pattern is most likely due to the main tidal channel responding to hydraulic changes, forced by back-barrier migration of tidal channels, and the flood tidal delta as the tidal prism changed through time. The hydraulic changes were a function of sediment influx from (natural and unnatural actions), transported by wave and current interactions from varying atmospheric events, with the most extensive morphologic changes observed in the most highly energetic states, hurricanes, and extratropical-like storms. Specific meteorologic events with varying wind direction and magnitude induced the reversal of tidal and longshore currents, leading to a confluence of currents within the inlet, which supported deposition in specific locations on the flood-tidal delta, middle shoals, and ebb-tidal delta. These meteorologic events led to changes in volumetric discharge through the inlet, causing other erosion areas (inlet thalweg). Sedimentological and foraminiferal data show that important parameters of sediment and foraminiferal transport are storm track, size, and magnitude, which can result in the forcing of heightened sea states, causing onshore transport. The digital stratigraphic model and geophysical data moderately agreed on general locations of relict geomorphic features that suggest similar morphodynamic evolution to what is observed in the geospatial data, corroborated by the hydrodynamic data. However, the novel stratigraphic model and the acoustic sub-bottom data were not expected to agree entirely due to one year, five months, and five days between the data used in creating the stratigraphic model and the data collected with the sub-bottom profiler. The geophysical data and stratigraphic modeling also suggest hydrologic conditions that would transport a specific grain size to the deposition locations, creating the features observed in corresponding locations. The results together help constrain the inlet's evolution through time, varying atmospheric conditions, and human influences.

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 score0.797
Threshold uncertainty score0.408

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.011
GPT teacher head0.234
Teacher spread0.223 · 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".

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Citations0
Published2023
Admission routes1
Has abstractno

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