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Record W2306444877 · doi:10.4043/26759-ms

Berth Mooring Influenced by Passing Effect of Ships in a River Channel

2016· article· en· W2306444877 on OpenAlexaff
C. Barclay, R. Cove, Albert W Y Chan, Charles Simmons

Bibliographic record

VenueOffshore Technology Conference Asia · 2016
Typearticle
Languageen
FieldEngineering
TopicWave and Wind Energy Systems
Canadian institutionsIntecsea (Canada)
Fundersnot available
KeywordsFenderMooringMarine engineeringChannel (broadcasting)Line (geometry)EngineeringStructural engineeringElectrical engineeringGeometry

Abstract

fetched live from OpenAlex

Abstract In this paper, a berth mooring for a vessel at a terminal is evaluated by analytical studies. The passing induced loads and motions are calculated for representative vessels moored for offloading in a river channel. The channel is modelled as a prismatic geometry. Passing induced loads are calculated using the ROPES software and are interfaced with ANSYS AQWA with which a series of dynamic mooring simulations are conducted for the moored vessel. The passing induced mooring line tensions, fender reactions, and vessel motions, velocities and accelerations at the moored ship's manifold are evaluated for a series of load conditions and mooring designs. The effects of changing some of the passing parameters on the mooring loads and motions are also evaluated and discussed briefly. Finally, the risks associated with damage of a single line are reviewed and the means to mitigate this risk are commented upon.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.006
GPT teacher head0.200
Teacher spread0.193 · 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 source (direct Gemma or distilled Codex), 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
Published2016
Admission routes1
Has abstractyes

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