MétaCan
Menu
← Back to cohort
Record W4200040630 · doi:10.1155/2021/5069859

Analysis and Modelling of Ship Manoeuvring Simulation in Landslide-Generated Waves

2021· article· en· W4200040630 on OpenAlexvenueno aff
Peiyin Yuan, Yu Zhao

Bibliographic record

VenueJournal of Advanced Transportation · 2021
Typearticle
Languageen
FieldEnvironmental Science
TopicLandslides and related hazards
Canadian institutionsnot available
FundersNational Natural Science Foundation of China
KeywordsLandslideGeologyThree gorgesWater levelGeotechnical engineeringSection (typography)Wave heightWaves and shallow waterSeismologyGeography

Abstract

fetched live from OpenAlex

The geological conditions of the Three Gorges Reservoir Region are complex and changing, and large- and medium-sized landslides are widely distributed. When a high-speed moving landslide enters the water, the water is significantly disturbed, and a landslide-generated wave will be formed, which will spread along the upstream and downstream of the river, causing significant threats and destruction to the hydraulic structures and the navigation of ships. Based on the typical rock landslide parameters and fracture development, we establish a three-dimensional physics experimental model of the bending section of the landslide-generated wave in the Three Gorges Reservoir Region. This paper primarily studies the variation law of the first wave height of landslide-generated waves with the width, height, and water entry velocity of the landslide body and then provides an empirical formula for the first wave height of landslide-generated waves in the curved section of the Three Gorges Reservoir Region. The ship rolling motion equation in the landslide-generated water area is analysed and established systematically. Additionally, the ship manoeuvring motion model in the landslide-generated water area is built. This paper explains the variation characteristics of ship turning tracks at different sailing speeds and sailing positions and proposes a basis to determine the navigation safety of ships in this area, thus providing new theoretical and technical support for the risk assessment of navigation of ships in the reservoir area.

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)0.000
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 score0.027
Threshold uncertainty score0.054

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0010.000
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 head0.013
GPT teacher head0.239
Teacher spread0.226 · 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

Citations2
Published2021
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Advanced Transportation→Same topicLandslides and related hazards→French-language works237,207→