Bibliographic record
Abstract
The Institute for Ocean Technology along with the Memorial University of Newfoundland completed phase 1 of Validation and Accreditation of Simulator Training (VAST) field trials. The field trials were completed in May 2010 in Holyrood, Newfoundland, Canada. Although simulation training in many other areas has existed for many years, it has only recently become a part of marine safety and therefore very little research has been done in this area. The VAST study proposed to examine how simulated lifeboat navigation training may improve performance in emergency evacuation situations involving ice covered waters, compared to those trained under standard training regimes. 19 Naive Participants were recruited, trained, and completed a series of tests, which involved operating a totally enclosed motor propelled safety craft through a simulated ice field. The 19 participants were divided into threes groups which each received a different form of training. Participants from groups 1 and 2 received a traditional form of training, where as the participants from group 3 were trained solely using a davit launched lifeboat simulator. The preliminary results for the performance data for one participant in each of three groups will be presented in this report. The human data acquired from the trials is not yet in a form capable of being analyzed. Thus far all the preliminary results from rudder executions, DGPS lifeboat paths and impacts indicate that group 3 appears to have more control over the movement and responsiveness of the lifeboat. Therefore they are able to completed manoeuvres that ultimately avoid major impacts and potential damage to the lifeboat.
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 | 0.014 | 0.014 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.016 | 0.005 |
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".