Bibliographic record
Abstract
This study is intended to propose a detailed execution plan regarding education and training for ensuring and verifying seafarers’ competence in relation to the introduction of a new small vessel operator certification system for vessel of less than 5 Gross Tonnage(GT). The methods of this study adopted include analysis of statistics of marine accident occurrence rate and causes relating thereto involving vessels of less than 5 GT, questionnaire survey and analysis for seafarers on small vessels and their shipowners and case study of Taiwan, Japan and Canada and literature survey. Together with the introduction of certification system for small vessels of less than 5 GT the seafarers’ training courses need to be differentiated depending upon whether an applicant has seagoing experience or not. The examination for the certificate for small vessel of less than 5 GT need to be conducted concurrently during the period of training courses in order to ensure soft landing of this new certification system. To do this, it is necessary to consider introducing actual ship training as well as training utilizing simulators. The follow-up action such as the revision of relevant laws and regulations, obtaining social consensus, etc. needs to be undertaken in order to implement the result of this study.
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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".