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Record W2913777897 · doi:10.9753/icce.v36.sediment.5

OBSERVATIONS OF BEACH-DUNE MORPHOGOLICAL RESPONSE TO STORM WAVES USING LIDAR BATHYMETRIC MAPPING IN A WAVE BASIN

2018· article· en· W2913777897 on OpenAlex
Ramy Y. Marmoush, Ryan P. Mulligan

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueCoastal Engineering Proceedings · 2018
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicAeolian processes and effects
Canadian institutionsQueen's University
Fundersnot available
KeywordsBathymetryStormGeologyLidarErosionGeomorphologyStructural basinRemote sensingOceanography

Abstract

fetched live from OpenAlex

Waves during major storms can cause significant changes to coastal morphology (Lee et al., 1998). The beach-dune system is known to be highly vulnerable to erosion when the wave run-up exceeds the threshold of the base of the dune in the collision regime, according to the Storm Impact scale defined by Sallenger (2000). Detailed bathymetric measurements are very difficult to obtain during storms due to the hazardous wave conditions. However, bathymetric surveys can be easily and intermittently performed during smaller scale physical model experiments (e.g., Hamilton et al., 2001) and high resolution can be achieved using laser scanning with Light Detection and Ranging (LIDAR) sensors (Smith et al., 2017). In the present study, a laboratory experiment of beach-dune morphology change is conducted in a rectangular wave basin that has recently been used to simulate erosion of a 2-dimensional sand dune (Berard et al., 2017). The objective of the present study is to investigate the 3-dimensional morphologic response of a sand beach-dune system to storm waves approaching at an oblique angle.

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.

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)0.001
Version: codex-gemma-dda1882f352aValidation 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 score0.308
Threshold uncertainty score0.736

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.031
GPT teacher head0.218
Teacher spread0.187 · 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