Education of maritime doctoral students as part of international university network
Bibliographic record
Abstract
Aalto University, together with four other universities, has received significant funding for an international Research Centre of Excellence for Arctic Shipping and Operations from Lloyd's Register Foundation (LRF), a UK registered charity that invests in science, engineering and technology for public benefit, worldwide.. The Centre is headed by the Aalto University (Finland), in partnership with the Hamburg University of Technology (Germany), the University of Helsinki (Finland), the Norwegian University of Science and Technology (Norway) and the Memorial University of Newfoundland (Canada). The Centre will support 15 doctoral dissertations on risk in Arctic operations over a period of five years. The Centre is the first of its kind in the Nordic Countries and the only centre concentrating on Arctic challenges. The funding provided by LRF is a very significant contribution to the activities of the consortium allowing it to continue to develop global expertise in the challenging operational environment owing to the long distances involved, the large fluctuation in ice conditions, and the unpredictable behaviour of ice. The breakthrough innovations are only possible through on-going research and development, in which theoretical modelling, laboratory tests and full-scale observations are in balance, which can be achieved most efficiently by international networking. The mobility of the doctoral students has key importance for the success of the project. This paper will describe the basic principles of organizing the mobility of the doctoral students and analyse the first experiences gathered from this mobility. The network of the students and their thesis topics will be described together with main links between the studied topics so that the overall challenging targets of the research activities can be achieved.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".