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Record W277746460

Modeling of Waves, Hydrodynamics and Sediment Transport for Protection of Wetlands at Braddock Bay, New York

2015· article· en· W277746460 on OpenAlexaboutno aff
Zeki Demirbilek, Lihwa Lin, Earl J. Hayter, Colleen O Connell, Michael C. Mohr, Shanon Chader, Craig M. Forgette

Bibliographic record

VenueUS Army Corps of Engineers: Engineer Research and Development Center (Knowledge Core) · 2015
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicAquatic and Environmental Studies
Canadian institutionsnot available
Fundersnot available
KeywordsBayWetlandEstuaryBay mudSedimentOceanographyEnvironmental scienceShoreGeologySediment transportErosionHydrology (agriculture)GeomorphologyEcologyGeotechnical engineering
DOInot available

Abstract

fetched live from OpenAlex

Abstract : This report describes a numerical modeling study of waves, currents, and sediment transport at Braddock Bay, New York, which are affecting the wetlands in this estuary. The wetlands have had damage by waves that penetrate deep into the bay from the Lake Ontario side. The purpose of this study was to investigate three proposed alternatives using different structural systems at the entrance of Braddock Bay to minimize the impacts of environmental forces on wetlands. Braddock Bay has had steady erosion and retreat of the shorelines outside and within the bay system in the last century. The bay complex in the present state has become fully exposed directly to the winds and waves from the lake side. The proposed structural alternatives at the bay entrance were evaluated on their ability to reduce potential impacts of waves and currents on wetlands. Study results indicated all three proposed alternatives were able to reduce waves, currents, and sediment transport substantially in the bay. The primary goal of the study was to develop a quantitative estimate of waves and flow in the bay for a relative comparison of the alternatives investigated.

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.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.786
Threshold uncertainty score0.633

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.103
GPT teacher head0.260
Teacher spread0.157 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
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
Published2015
Admission routes1
Has abstractyes

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