Design, Fabrication, and Test of a Single Rotor Modular Unmanned Aerial Vehicle for Algae Bloom Monitoring of Lake Erie
Bibliographic record
Abstract
Every summer, runoff pollution is causing algae in Lake Erie to grow out of control, impacting the health of the lake, suffocating fish, making water unsafe for swimming, deterring tourists, and damaging local economies. Given these facts, the current study proposed a swarm of single rotor unmanned aerial vehicles (SRUAV) for health monitoring of Lake Erie. Traditionally, for such a task, a single drone is designed with complicated structure and control modules resulting in high costs of design, construction and maintenance. A single unit design can be very vulnerable and costly to maintain. Robotic swarms can achieve the same ability through cooperation and have the advantage of reusability of the simple agents and the low cost of construction and maintenance. Robotic swarms also have the advantage of high parallelism, which is especially suitable for large scale tasks. In the present work, as the first phase of the overall project, design, fabrication and test of a single agent from the envisioned swarm is detailed. The simple agent will be equipped with a modular payload fitted with either a camera or sampling/dispenser device and will be responsible for the aerial photography and sampling of algae blooms in Lake Erie. The current practice for the research data collection is either relying on the US-based research centers data or conducting manual field investigations. The long-term goal of the proposed research is to provide an alternative low-cost solution for the health monitoring of Lake Erie, with other potential use cases, which could benefit local Canadian researchers including UWindsor's Great Lakes Institute for Environmental Research and enhance the productivity and efficiency of the monitoring practices.
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.
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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".