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Record W7025119027

Temporal Variability of Sea-Bed Morphology: Insights from 24 Years of Multibeam Surveys Near New York

2021· article· en· W7025119027 on OpenAlexaboutno aff

Bibliographic record

VenueUniversity of New Hampshire Scholars Repository (University of New Hampshire at Manchester) · 2021
Typearticle
Languageen
FieldPhysics and Astronomy
TopicX-ray Spectroscopy and Fluorescence Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsSeabedEcho soundingBathymetryMarine geologyFishingBenthic zone
DOInot available

Abstract

fetched live from OpenAlex

The wide-spread use of the multibeam echosounder in the ocean environment has revolutionized our understanding of the nature of the seabed and especially the variety of processes active on the seabed and the role of the seabed in the development and characterization of benthic habitats. In U.S. nearshore areas much of this data is being collected by NOAA as part of their charting mission, with more mapped sea floor being added to the inventory each year. The effort to map the sea floor is extending world-wide through the very exciting SeaBed 2030 initiative. Charting activities and SeaBed 2030 properly focus on mapping the seabed to show current conditions but there is much interest about how seabed conditions change over time. New York has a fairly long coastline that includes the Atlantic Ocean, Long Island Sound, New York Harbor, the Hudson River and the Great Lakes Ontario and Erie. SoMAS (School of Marine and Oceanographic Sciences) at Stony Brook University has been operating a Kongsberg multibeam echosounder (EM3000/3000D) since 1998 and has done surveys off the coasts of eight U.S. States and off Turkey in the Black Sea. New York surveys have been done on the Atlantic inner shelf, south of Long Island, in Long Island Sound, and in the Hudson River, and were often in support of local agency needs such as artificial fishing reefs, environmental regulation and shellfish management. While these surveys may have been routine in nature, they did provide new information about seabed morphology at the time of the survey which has led to new ideas and also allowed for the development of informed management strategies. Many of our surveys occurred before the areas were surveyed by NOAA using multibeam techniques. We have also revisited some of our survey areas on several occasions and others groups, including NOAA, have also collected new data in some of these areas. As a result, there are a number of areas along the New York coastline where high-resolution bathymetry and backscatter data have been collected on multiple occasions over timescales of up to 20 years. The objective of this presentation is to show several examples of how repeat surveys over several time scales using in part SoMAS survey data demonstrate the variability of sea-floor features and, although environmental data is imperfect, provide insights into the physical and biological processes that have affected the seabed and sand transport in the inner shelf. While the Sandy storm had a measurable effect on seabed morphology in our inner shelf study areas, many other events have also been important in modifying the sea floor over time. Presenter Bio Dr. Roger Flood got intrigued by hydraulics, sediment transport and bedforms in sandy sediment as an undergraduate at MIT. He spent a summer at WHOI and learned about the wide range of sedimentary features in the deep sea, including the poorly understood Lower Continental Rise Hills which were one of the prominent features on Marie Tharpe's drawings of the ocean floor. Roger went on to get his Ph.D. in Marine Geology in the MIT/WHOI joint program in Oceanography in 1978, and then did a postdoc year in England at the shallow-water branch of IOS with Keith Dyer. At IOS, he learned more about sediment transport and bedforms in coastal environments, and then went to Lamont-Doherty to resume his deep-sea activities. At Lamont, Roger had two cruises in the mid-1980s using the R/V Contrad's new 16-beam multibeam system to study channels on the Amazon Fan and sediment waves in the deep sea. In 1988, Roger started working at Stony Brook University on Long Island, NY, where Larry Mayer and Dale Chayes worked with him to convince ONR to buy an EM 3000 multibeam system, which has been used in maybe as many as 50 deployments. Many of the projects undertaken have supported New York DEC and other agencies in their management of the marine and fresh-water environment and helped to provide Roger an evolving understanding of sea-floor processes.

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How this classification was reachedexpand

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.113
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.016
GPT teacher head0.204
Teacher spread0.188 · 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 teacher head, not a consensus.

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".

Quick stats

Citations0
Published2021
Admission routes1
Has abstractyes

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