A Concept of Coastal Sea Monitoring System from Sky to Water
Bibliographic record
Abstract
In this paper, at first, we show our recent monitoring result using a multi-copter at Miho Island's beach as an example of Japanese coastal beach erosion. Comparing two videos in 2016 and 2017, we found massive sand can be easily washed away by only one typhoon and making artificial beach by putting sand for ten years proved to be failed. We also found wave dissipating blocks didn't work well. From this experience, we felt strong demand to develop a new device which we can monitor the coastal area from sky to underwater. So in this paper, we present a concept of environment monitoring system for coastal sea area. The system consists of multicopter, unmanned surface vehicle (USV), unmanned underwater vehicle (UUV), and floating LBL system to record UUV's underwater position. The main characteristics of this system is USV and UUV are combined together and a multi-copter transports this USV/UUV system from shore or boat to the site where underwater the monitoring is desired. We call this concept as sky to water system (STW). To verify our STW concept, we designed and fabricated a small ROV and USV which can store the ROV. As the payload of the multicopter is limited, the combined STW weight must be within its payload. We deployed DJI S1000 multi-copter whose maximum payload was around 80N when we boosted its battery, so the STW system must be fabricated as less than 80N. We conducted an experiment to verify our concept and it succeeded with some lessons learned.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".