Bibliographic record
Abstract
The Atmospheric Diving Suit (ADS) has a longstanding history of use to aid in subsea construction, salvage, repair, platform inspection, oil and gas operations, and scientific purposes such as examining shipwrecks. The emergence of Remotely Operated Vehicle (ROV) technology, and other unmanned underwater vehicles, have begun to supplement the ADS in certain applications. However, due to the complex nature of subsea tasks, the tethered ADS still stands as a strong contender in the realm of manned and unmanned submersibles, especially when weighted against its cost and dexterity. The Next Generation ADS not only offers an alternative to traditional diving or ROV use, but bridges the gap between them. While there are clear advantages of using an ADS, such as mitigating risks associated with decompression, there are also drawbacks, including the risk of having a diver at the work site. While some risks can be mitigated with modern technology, there are still challenges that lie ahead for ADS design. The use of state-of-the-art equipment, advances in subsea technologies, and a summary of the successful missions are presented, to provide a comparison to past technologies and current ADS improvements and application advantages.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.022 | 0.008 |
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".