Collector: A Vision-Based Semi-Autonomous Robot for Mangrove Forest Exploration and Research
Bibliographic record
Abstract
Sundarban, the largest mangrove forest in the world and one of the UNESCO's World Heritage site is considered to be in danger due to the effect of climate change which may lead to possible extinction of its wildlife such as Royal Bengal Tigers, due to the recent establishment of coal-fired power station near the forest and other man-made and natural hazards. To protect and save the forest, there is not enough data for researchers to understand the change of the micro-climate and how it's affecting its fauna and flora. Moreover, to understand how the coal-fired power plant is affecting this forest, the forest has to be constantly monitored. As of now, there is no constant patrolling robot specialized for mangrove forest or research program that is functioning in Sundarban, Bangladesh sector. To mitigate this problem, we propose a mangrove forest research robot that can traverse both semi-autonomously and manually to constantly monitor the forest. The robot collects weather data as well as PH level of soils and water, CO level, SO, air quality and water quality data and store in memory on the robot which can later be analyzed by the researchers accordingly. The paper also introduces a novel wheel design that can traverse in forest terrain. The robot is also able to make sense of its surroundings using computer vision. And finally, it is equipped with a LIDAR to measure forest density by 3D mapping. Overall, the paper proposes the design and implementation of a research robot that is specially designed for the mangrove forest environment.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 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".