Mooring developments for autonomous ocean-sampling networks
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Two general-purpose mooring designs have been developed to support autonomous underwater vehicle (AUV) operations in autonomous ocean sampling networks (AOSNs). These moorings provide two-way communications between investigators and AUVs docked on the moorings or conducting survey operations some distance from the moorings. A deep-water design that incorporates an AUV dock and recharging station was built for use in the Labrador Sea during the winter of 1997/1998. This severe winter environment required a robust design that could operate unattended for six months while isolating the dock from surface wave motion. A much lighter, easier-to-deploy design was developed for use in coastal waters to extend the nearshore AOSN operating area by extending the communications network. This coastal design has been deployed without the dock component and has typically been configured for use in a small network of moorings maintained with a small research vessel. The deep-water mooring has been deployed successfully on two occasions, for short periods of time. The coastal moorings have been deployed a number of times and have proven to be quite effective. This paper describes the two moorings in detail and provides information on their performance so that interested investigators can utilize the technology where it meets their needs.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it