The High Capacity Expanding Lifeboat HiCEL – Meeting the Modern SAR Challenge
Bibliographic record
Abstract
The Mediterranean migrant crisis has resulted in the highest population displacement since the Second World War. In 2016 alone, over one million made the journey across the sea. Since 2013 over 15,000 have died as a result of this journey. Small vessels such as wooden fishing boats and RIBs are commonly used by smugglers as transport. These are often unseaworthy and filled with numbers of passengers far exceeding their intended capacity. When failure occurs, rescues are typically conducted by the nearest available vessel. These vessels are often ill-equipped for a large-scale Search and Rescue (SAR) operation making it highly dangerous for all involved. The size and quantity of lifeboats available are often insufficient for the large numbers of people to be rescued; as a result, repeat journeys are required, making the rescue process slow, inefficient and hazardous. This paper outlines a novel solution to this problem. A concept design is presented for a rapidly expandable lifeboat capable of holding large numbers of passengers, whilst still fitting into the operational envelope of common davits. The unique inflatable design can be deployed quickly from a range of vessels and aeroplanes offering an immediate platform from which disembarkation onto a suitable vessel can be achieved. CONOPS are outlined along with the required capabilities of the design. Drop stitch technology is identified as a viable means of manufacturing the large inflatable platforms. Finally, the paper discusses an alternative solution, retrofitting existing enclosed lifeboats with the solution to offer a more cost-effective alternative.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".