In-situ Geotechnical Investigation of Coastal Sediments with Regards to Sediment Remobilization Processes and Sediment Stability
Bibliographic record
Abstract
Geotechnical parameters and soil behavior of coastal sediments impact sediment remobilization processes and stability. Therefore, they have the potential to be a key factor for the assessment and prediction of coastal erosion, coastal engineering activities, as well as naval applications. Most geotechnical in-situ methods have been developed for non-subaquaeous conditions or for offshore conditions with the availability of ship support. However, few field techniques have been developed for the coastal zone with strongly varying water elevations and energetic hydrodynamics, leading to a current gap in data availability and understanding of soil behavior in the intertidal and nearshore zones. This presentation provides an overview into most recent developments of portable free fall penetrometers for the in-situ characterization of surficial sediments in coastal areas, the use of wave gauges for pore pressure measurements in the coastal zone, and the use of remote sensing techniques for geotechnical characterization of beach sediments. Data examples will include results from field expeditions to the Arctic, Duck, North Carolina, Sylt, Germany, and Yakutat, Alaska, giving new insights into the relationship between soil behavior and coastal processes, but also raising new questions. Presenter Bio Nina Stark received a Diploma (MSc) in Geophysics in 2007 from the Westphalian Wilhelms University of Muenster, Germany. For her thesis (“Characterization of seafloor sediments using eXpendable Bottom Penetrometer) she collaborated with Dr. Thomas Wever from the Institute for Underwater Acoustics and Geophysics of the German Navy in Kiel. She finished her PhD in Marine Geotechnics in 2011 at MARUM-Center for Marine and Environmental Sciences at the University of Bremen, Germany. She worked on the “Geotechnical investigation of sediment remobilization processes using dynamic penetrometers” under the supervision of Prof. Achim Kopf. She was a postdoctoral fellow in 2011 in the marine geotechnics group at MARUM under supervision of Prof. Achim Kopf, and from 2012-2013, in the physical oceanography group at Dalhousie University, Halifax, Canada, under the supervision of Dr. Alex Hay, before being appointed assistant professor in the Charles E. Via, Jr., Department of Civil and Environmental Engineering at Virginia Tech in Fall 2013. Her research focuses on coastal and marine geotechnics including instrument development, the geotechnical investigation of subaqueous sediment dynamics, beach dynamics, ocean renewable energies, navigation channel deepening and maintenance, beach trafficability, and remote site characterizations in coastal environments. She developed the free-fall penetrometer “Nimrod” in the framework of her PhD, and collaborated with BlueCDesigns on the design of “BlueDrop”. To-date she has conducted more than 50 field surveys in the North Sea, Baltic Sea, Northern Atlantic, Arctic, Northern, Central and Southern Pacific, as well as in a number of lakes and rivers. Nina has received the NSF CAREER award and the ONR Young Investigator award in 2018.
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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 | 0.000 | 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".