Bibliographic record
Abstract
AbstractThe world of manned underwater vehicles (MUV) in 2013 counts a total of 95 active submersibles used for ocean research, tourism, and commercial, leisure, and security applications. The MUV industry safety record remains pristine, with not a single incident involving loss of life in over 40 years. The paper reviews the state and future directions for the world’s deepest ocean research submersibles. In 2012 and 2013, the world of deep research submersibles saw dramatic advances, reaching full ocean depth for the first time in more than 50 years. The record of the world’s deepest submersible held by Japan’s Shinkai 6500, rated to 6,500 m depth, for almost 25 years was surpassed by China’s 10-year development project of the Jiaolong submersible, rated to 7,000 m. The Jiaolong successfully completed its multiyear testing program, achieving full design depth in June 2012. This record feat was eclipsed, however, by the surprise disclosure and full ocean depth dive by James Cameron’s Deepsea Challenger, diving to a depth of 10,908 m in the Mariana Trench just months earlier. This depth had not been reached since the historic dive of the bathyscaphe Trieste in 1960, and brings new energy for several national organizations to develop full ocean depth submersibles. There are today a total of 14 national and commercial submersibles capable of diving 1,000 m or deeper, offering a wide range of services. This also provides a global network of rescue capability with locations in the United States, China, Japan, Russia, France, Spain, Canada, and Portugal.
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".