MATE ROV Competitions Bring Ocean Science and Technology to Students and Educators across the U.S. and Canada
Bibliographic record
Abstract
Despite our nation's increasing reliance on the ocean environment, students and educators are often unaware of ocean career opportunities and the knowledge and skills required to enter those careers (Sullivan et al., 2006; Sullivan, 2002). A consequence of this lack of awareness is a shortage of skilled individuals who can fill ocean workforce needs (MATE Forum, 1996; Zande & Sullivan, 2003). The Marine Advanced Technology Education (MATE) Center and the Marine Technology Society's (MTS) ROV Committee created the remotely operated vehicle (ROV) competition to address this issue and bring ocean science and technology to students and educators across the U.S. and Canada. Since 2001, the MATE Center and the ROV Committee have engaged thousands of students and educators from middle schools through universities in developing ROVs for tasks based on real workplace situations. In doing so, the program has promoted ocean issues and careers, connected students and educators with employers and working professionals, and helped students to develop valuable technical, problem solving, and teamwork skills.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.050 | 0.006 |
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".