ROV (<i>Suijin</i>)
Bibliographic record
Abstract
AbstractThe 2009 Marine Advanced Technology Education (MATE) remotely operated vehicle (ROV) competition focuses on a submarine rescue training exercise. There are four tasks outlined for the Ranger class, each providing its own challenge. Our ROV was designed to carry out these tasks with precision and agility.The team spent numerous hours planning, building, and field testing our ROV. We had to be prepared to combat technical problems and overcome the challenge of differing opinions. Because of the diversity of the tasks, Suijin had to be very well designed. This required the creation of a rigid frame, useful end effectors, and a versatile propulsion system; a form of buoyancy, effective sensors, and proper wiring were also necessary. There were many ideas to consider and obstacles to overcome, but finally, we completed our masterpiece.Heritage Robotics is very pleased to present the following technical report, which communicates the details of Suijin, an ROV created by students from Heritage Collegiate, Lethbridge, Newfoundland, Canada. This document includes detailed descriptions and diagrams of Suijin’s components, possible future improvements, trouble shooting techniques, the lessons we learned, the challenges we faced, information on the Submersible LR5, reflections, a thorough budget, and acknowledgments of all those who helped along the way.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.451 | 0.235 |
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".