Bibliographic record
Abstract
Many linear search and evacuation problems have been studied on robots that move with unit speed seeking to evacuate through an unknown exit located on a line.It is interesting to study the linear search and evacuation problems on robots with variable maximum speed in order to compare the results based on that speed.What makes the problem more interesting is to include a passive entity, such as a bike, that can be used by any of the robots.This will make the problem more generalized and thus it may encompass many previously done studies on linear search and evacuation, as we will demonstrate later.We will revisit the linear search and evacuation problems; however, this time we will use two robots with a bike under the condition that only one robot can use the bike at a time.The exit would be placed at an unknown position on the line.The direction that the robot should follow to reach the exit is unknown.The problem will be divided into two categories: linear search and evacuation.Evacuation in turn will be studied for two communication models: wi-fi and face-to-face.Regarding the linear search problem, we have shown two different algorithms that are optimal (relative to the upper bound) based on the maximum speed.Additionally, we provided a section related to the lower bound.Regarding the wi-fi evacuation model, we have shown three different algorithms, two of which are optimal (relative to the upper bound) based on the maximum speed.Furthermore, we have also provided the lower bound for the wi-fi model.Regarding the face-toface model, we have shown three different algorithms (relative to the upper bound), one of which is optimal regardless of the value of the maximum speed.Many graphs have been provided to illustrate how each model performs.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".