Legal Approaches to Dry Cargo Liquefaction: An Arctic Perspective on a Global Problem
Bibliographic record
Abstract
Purpose—The liquefaction of dry cargoes poses a serious threat to maritime safety. Dry cargo liquefaction is frequently the cause of loss of life at sea. This text aims at raising awareness of the utility of existing international law norms to contribute to disaster risk reduction (DRR) at sea in this particular context. Design, Methodology, Approach—The topic is approached from a particular Arctic perspective as the Arctic Ocean is opening up for maritime traffic in ways never seen before. Findings—By bringing together technical and legal aspects, the text provides the reader with insights into a challenging problem with high practical relevance for seafarers around the world, emphasizing the human dimension of the regulation of the use of maritime spaces. Practical Implications—This approach highlights the practical importance of insurance providers and other actors for enhancing shipping safety. This role can be seen also in other aspects of shipping safety, for example with regard to oil pollution or passenger rights. Originality, Value—At this time, it appears that Arctic-related seafarer training regimes are not yet taking the increased risk of Dry Cargo Liquefaction into account as a matter of course—nor is there a corresponding legal requirement de lege lata. Nevertheless, awareness of Arctic conditions and risks can help increase awareness of specific Arctic risks among crew members. There are not specific DCL-related rules in the Polar Code but it learning about Arctic-specific risks can complement existing rules, such as those of the IMSBC Code, to enhance seafarer safety.
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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.008 | 0.016 |
| Scholarly communication | 0.013 | 0.009 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.005 | 0.009 |
| 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".