Bibliographic record
Abstract
Fugro Survey and ISE are developing a modular AUV for deep water, commercial site survey. The Explorer AUV is intended initially for operations in depths of up to 3500 meters where it will conduct seabed surveys more economically than deep tow systems. Explorer will carry a full suite of seabed survey equipment including a multibeam echosounder (swathe bathymetry), dual frequency sidescan sonar and subbottom profiler, magnetometer, and conductivity temperature and depth probe (CTD). The vehicle will have a top speed of 2.5 meters per second and a range of 300 km with the capability of upgrading the range to 750 km with a fuel cell. Throughout its survey mission, the vehicle will maintain a navigational accuracy sufficient to meet the oil industry requirement of data positioning accuracy within 5 to 20 meters. Development of the vehicle commenced in the summer of 1999. In this paper, the authors review the factors and trade-off considerations which led to the selection of the Explorer vehicle configuration, pressure hull design, power source, control, navigational and positioning, sensor data management and acoustic telemetry, and finally, the approach to launch and recovery.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".