Berm migration and munitions motion under scaled storm events
Bibliographic record
Abstract
From the time of the First World War until 1970, unused munitions used to be disposed into the sea. A century later, these unused munitions are still becoming exposed onshore endangering the public and marine life. The migration and exposure of these unused munitions in the nearshore under extreme events is poorly understood. In the United States, coastal regions are home to about 128 million people or nearly 40% of the whole population. Coastal erosion will continue to worsen as storms intensify due to sea level rise driven by climate change. As a result of the erosion of natural beach defenses, infrastructure and populations close to coastal areas will endure flooding. It’s crucial to predict the migration of geomorphological features such as berms in order to understand the erosion processes. The goal of this study is to take an initial look at how to bring these two topics, berms and munitions, together by studying the processes that drive munitions of variable density to migrate and bury in the berm. A large-scale experiment at Institut national de la recherche scientifique (INRS) in Quebec City, Canada was conducted to study these processes. Mantoloking Beach, NJ and Hurricane Sandy were scaled to replicate the beach profile and wave conditions, respectively. One hundred fifty-five munitions of variable density were deployed for this experiment. Three cases from the experiment were analyzed for this study: a low-forcing case, a high-forcing case, and a longer-period wave case. In addition to the forcing conditions established during each case, the root mean square wave height was calculated to understand the hydrodynamics for each of the 3 cases. The force going into the swash zone was compared to the accretion and erosion found in the berm. The greater the force going in, the greater the accretion or erosion found in the berm. Less dense munitions had greater net migrations than their denser counterparts. Munitions deployed on the berm crest migrated onshore while the munitions starting on the berm face migrated offshore.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".