Demo abstract: R.A.V.E.N. — Remote autonomous vehicle explorer network
Bibliographic record
Abstract
Unmanned aerial vehicles (UAVs) have recently become a viable platform for surveillance and exploration tasks. Several commercial quadrotor aircraft have been successfully used as surveillance equipment with groups such as United States and Canadian police forces, and additional applications for this technology could include exploration of ra-dioactive/hazmat environments, naval search and rescue, or surveying a building on fire, to name a few. Despite the agility and speed of the quadrotor platform, current systems lack the redundancy and collaboration of a multi-unit team; current implementations of quadrotor UAV flocks require expensive equipment, limiting the system to operation within range of external sensors. We propose a system for intelligently controlling multiple quadrotor UAVs using a combination of on-board vision tracking and wireless communication of attitude measurements. The proposed system uses a lead, human-controlled quadrotor and one or more quadro-tors that track and follow the lead unit autonomously. The forthcoming system aims to improve the execution time required to complete missions and increase both breadth of search and platform effectiveness.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.134 | 0.042 |
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".