Coordination of an underwater glider fleet for adaptive sampling
Bibliographic record
Abstract
Prediction (ASAP) projects aim to develop a sustainable, portable, adaptive ocean observing and prediction system for use in coastal environments. These projects employ, among other observation platforms, autonomous underwater vehicles that carry sensors to measure physical and biological signals in the ocean. The measurements from all sensing platforms are assimilated in real-time into advanced ocean models. The objective is to coordinate the mobile assets in order to collect data of highest possible utility. Critical to this effort are reliable, efficient and adaptive control strategies to enable the mobile sensor platforms to collect data autonomously. In this paper, we summarize feedback control strategies that enable us to gather useful information over a wide spectrum of spatial and temporal scales. First, we design formation control strategies useful for sampling small spatial scale processes (less than 5 km). In this framework, the feedback control laws maintain a desired formation of vehicles and allow the group to locate interesting features in the ocean. Some of these control strategies were implemented on a group of underwater gliders in Monterey Bay in August 2003, as part of the AOSN-II project. Second, we direct mobile sensor networks to provide synoptic coverage to investigate
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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 | 0.000 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".