Mobility and Burial of Munition Surrogates in the Inner Surf and Swash Zones
Bibliographic record
Abstract
Unexploded ordnance (UXO) resulting from past military activity are present in coastal settings. Mobility of UXO, specifically in the inner surf and swash zones, constitutes a potential risk for the public. Mobility or exposure may increase under energetic events due to enhanced forcing or sediment erosion. Yet, the conditions leading to exposure, burial, or movement of UXO remain poorly understood. A large-scale laboratory wave flume (120 m x 5 m x 5 m) study at the Institut national de la recherche scientifique (INRS) in Quebec City, Canada was carried out from July 7 to September 23, 2022 to quantify surrogate UXO mobility and burial. An undistorted, scaled beach profile from Mantoloking, NJ, USA was constructed using 0.28 mm diameter sand. Eighteen stations were established at roughly 5 m intervals to collect hydrodynamic, sediment process, and morphology data and to quantify surrogate UXO behavior. Over 150 surrogates of varying bulk density were distributed throughout the flume. Waves were forced in 300-wave packets for each trial of a condition case. Cases used different wave heights, water levels, and wave periods. Preliminary results indicate berm erosion with increasing hydrodynamic energy. The dune only experienced erosion at the highest water levels. Surrogate UXO generally remained in place and buried partially or migrated offshore. Migration tendency and distance was a function of the surrogate density.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".