Bibliographic record
Abstract
In January 2003 the Maersk Carolina, a Panamax container vessel, encountered a storm in the North Atlantic en route from Algeciras, Spain, to Halifax, Nova Scotia, Canada. The vessel experienced gale-force winds and seas in excess of 10 m. During one particularly violent rolling and pitching event, the vessel quickly and unexpectedly began rolling upwards of 47 degrees. During this incident 133 containers were lost overboard, and 50 others sustained moderate to severe water damage but remained on board. Cargo claims exceeded $4 million. The vessel itself sustained moderate structural damage. Results of a simulation and modeling study of the event are presented, and they indicate that the vessel had experienced parametric rolling, a little-understood and unexpected risk for certain container vessel designs. The simulation further showed that in tiers where containers were lost, excessive compression forces acting on the lower on-deck container tiers exceeded the strength of the corner posts, which would cause containers in these tiers to collapse and result in a catastrophic failure of the lashing system. All simulation results correspond closely to what was observed on the vessel. A brief introduction to vessel design and weather factors leading to parametric rolling is provided. New wave sensing technology, advanced on-board analysis software similar to that used in the Maersk Carolina study, and precise vessel motion measurement technology currently under development are described. These innovations will allow masters to make more informed navigational decisions and avoid parametric rolling events in the future.
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 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".