Revisiting the leeway of shipping containers: a case study of the M/V Zim Kingston incident
Bibliographic record
Abstract
Abstract On 22 October 2021, 109 shipping containers fell overboard from the M/V Zim Kingston in rough seas off the coast of Vancouver Island, British Columbia, Canada. While afloat, these shipping containers pose a significant risk to marine traffic in addition to being a source of marine pollution. Out of the 109 shipping containers, 4 were discovered on the beaches of northwest Vancouver Island 5 days later. Drift simulations were made using the standard leeway tables for shipping containers that vary with the immersion fraction of the shipping container. These leeway values over the expected range of immersion levels underestimated the travelled distance of the shipping containers relative to the observed grounding locations. An increase in the leeway of 1.5% of the wind speed improves the agreement between the simulations and observations, which is consistent with the addition of the Stokes drift to the leeway of the shipping container. It is argued that the leeway measured using the direct method, which was used to calculate the leeway of shipping containers, does not implicitly include the Stokes drift as previously suggested. This result suggests that the Stokes drift should be added to the leeway calculated with the direct method. While the error is small over timescales of 24 to 48 h, it accumulates in time and is appreciable for drift prediction greater than 48 h.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".