Bibliographic record
Abstract
The needs of undersea defense, and indeed those of the scientific community, overlap those of the offshore oil and gas industry, not least when it comes to subsea operations. They share problems encountered in the design and use of unmanned underwater vehicles, particularly in relation to reliability, efficiency, cost-effectiveness, and capabilities. The U.K. Marine Technology Directorate Ltd. (MTD) is managing a research program - Technology for Unmanned Underwater Vehicles (TUUV) - formulated with the particular aim of solving the problems identified by industry in relation to the operation of UUVs. The overall goal of the first three TUUV phases is to provide a convincing demonstration that the production of such advanced systems is practical, credible and cost-effective. Despite the advances in TUUV1, discussions with operators and program participants show that broad concerns over vehicle operations remain. TUUV2 and TUUV3 will transform the current purely technical and largely independent projects into an integrated concept demonstration that will justify the mainly industrial investment needed to make TUUV4 a reality in time for the millennium.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.004 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.010 | 0.004 |
| Insufficient payload (model declined to judge) | 0.991 | 0.993 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".