A hexagonal grid-based sampling planner for aquatic environmental monitoring using unmanned surface vehicles
Bibliographic record
Abstract
Unmanned Surface Vehicles (USV) with capabilities of mobile sensing, data processing, and wireless communication have been deployed to support remote aquatic environmental monitoring. This paper introduces a sampling planner for spatiotemporal survey of an aquatic environment using a USV-based sensing system. The sampling planner is proposed to distribute the Sampling Locations of Interest (SLoIs) over a geographical area and generate paths for the USVs to visit more SLoIs within their energy budgets. The sampling locations are chosen based on a cellular decomposition of uniform hexagonal cells. The SLoIs are visited and sensed by the USVs along a planned path ring, which is generated through a Spanning Tree-based Planning (STP) approach. To ensure that each SLoI measures within a certain time interval, multiple USVs are assigned to travel along the sub-paths that are divided from the generated path ring. In this paper, first an execution example presents the effectiveness of the proposed method. Then, the performance of the proposed sampling planner is demonstrated based on two application scenarios using USVs for aquatic environmental monitoring. The experimental results are presented in this paper.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".