Mooring developments for autonomous ocean-sampling networks
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.
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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 | 0.001 |
| 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 | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".