Model testing of Totally Enclosed Motor Propelled Survival Craft (TEMPSC): lifeboat performance in ice
Bibliographic record
Abstract
This report constitutes an Industry Company Profile as required by the Engineering Co-op Program. It is divided into three parts: an industry profile of the offshore safety research industry, a company profile of the Institute for Ocean Technology (IOT), and my role as an engineering work term student here at IOT. The industry profile describes the history of offshore evacuation and the tragedies that have propelled this industry to develop. Also discussed is the general structure and distribution of the industry, and several elite institutions that conduct offshore research around the world. The company profile aspect of this report includes a general history of IOT, as well as a description of the facilities and equipment that are currently available. In addition, I also concentrate on the role of the marine safety research team within the overall research and development mandates of IOT. The final section of this report deals with my role as an engineering work term student at IOT. Its main focus is to describe what it means to be an active member of the safety research team. I do this by describing several projects I was involved with during the work term, primarily the physical testing and data analysis phases of the model-scale lifeboats in ice project.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.002 | 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".