Analysis and Modelling of Ship Manoeuvring Simulation in Landslide-Generated Waves
Bibliographic record
Abstract
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.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".