The impact of underwater waves on ship manoeuvrability: a case study in a fjord
Bibliographic record
Abstract
In 2019, the Motor Vessel Jaeger Arrow collided with the Grande-Anse Terminal wharf (Saguenay Fjord, Canada) during docking from unknown causes. However, the timeline of the incident and the ship's behavior during docking suggest that underwater waves may have caused the collision. Data collected in 2023 using a camera and thermometers confirmed that this area of the fjord regularly experiences underwater waves with wavelengths ranging from 50 to [Formula: see text], wave heights of 1 and [Formula: see text], and periods of around [Formula: see text]. These waves frequently collide with and reflect off the wharf, generating currents of 0.1 to [Formula: see text]. Numerical simulations further illustrate the interactions between the waves and the wharf, highlighting regions near the wharf where wave-induced currents, both inshore and offshore, occur, including areas with near-zero currents that could create a false sense of calm conditions. Importantly, our observations also revealed that large ships, such as the Jaeger Arrow, can generate their own underwater waves, potentially compromising docking operations. While we cannot definitively confirm that underwater waves caused the incident involving the Jaeger Arrow, our study offers a plausible explanation: the ship may have been caught in a wavetrain reflecting off the wharf, leading to unpredictable movement during docking. These results highlight the potential risks posed by underwater waves to ship safety and maneuverability during docking operations, a topic under-explored in existing scientific literature.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.001 |
| 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".