Escape evacuation and rescue survivability testing
Bibliographic record
Abstract
As offshore petroleum exploration in Canadian waters grows continuously, the need for improved offshore safety rises also. The unique weather conditions (wind and waves, cold temperatures and ice) off the Canadian coast must be addressed. As part of the Escape, Evacuation, and Rescue team, I was involved in experiments at the Institute of Ocean Technology, which established the operational weather limits of Totally Enclosed Motor Propelled Survival Craft (TEMPSC). To do this, we used model survival crafts to perform typical tasks in a controlled environment of wind and waves in the Offshore Engineering Basin. Although the data and results have not yet been analyzed, several preliminary conclusions can be drawn. The MadRock model seemed to have better maneuvering in the weathers then the other two models. Also, it was found that the greatest setback occurred when the models were launched on troughs and up slopes. I would recommend that the research team improve the design of the model launch system be improved to allow the models to experience their natural motions without influence from the current system. I would also recommend that the wind machines be upgraded to minimize swirling.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".