Environmental Research Robots: A Student-Driven Initiative
Bibliographic record
Abstract
Abstract The College of the North Atlantic-Qatar (CNA-Q) is a satellite campus of a Canadian comprehensive technical college based in Newfoundland and Labrador, Canada. CNA-Q is in its tenth year with over 2000 students and 500 faculty members. In 2008, we began Phase I of our research programme. The main objective was to create a student-driven interdisciplinary learning environment. CNA-Q received a grant to design a prototype of an autonomous environmental research robot (ERR). The students agreed on an environmental issue and then worked as a team to design robots that would improve the situation. Their first ‘mission’ involves the endangered Hawksbill Turtle, which is indigenous to the Gulf region. In Qatar, the female Hawksbill turtle lays her eggs on the beaches of one of the largest industrial cities in the world, Ras Laffan, a producer of natural gas. The Hawksbill turtle is at risk for many reasons including: turtle meat and turtle eggs are considered a delicacy, natural predators and, our concern, the contaminated nesting beaches through toxic waste, excessive noise and distracting lights. Using Mindstorm robotic kits, students from Engineering, IT, Business and Health Sciences built over 100 robots looking for the best-designed and programmed robot, capable of withstanding the harsh environment as well as being sophisticated enough to collect and store data at given intervals and locations. To ensure that the students had the background necessary to understand the turtle's plight, they spent a week in Malaysia working with Dr. Nicholas Pilcher, founder of the Marine Research Foundation, Sabah including a stay at Borneo`s Turtle Island. In addition, for the past two years CNA-Q students have assisted with the satellite-tracking project at Fuwayrit Beach, Qatar. Phase II began in the fall of 2010. The first Environmental Research Robot was built, programmed and equipped with sensors. The robot did trial readings in the Spring of 2010. A second ERR, a cleanup robot, is in the planning stages. This paper will discuss the innovative teaching and learning practices that resulted from the melding of the research, technology and environmental aspects of this project.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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; both teacher heads agree on what is shown here.
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".