Bibliographic record
Abstract
This paper explores the problem of identifying the shapes of invisible hazardous entities in R2 by a set S = {s1, s2, . . . , sk} of mobile sensors (autonomous robots). A hazardous entity, H, is a region that affects the operation of robots that either penetrate the area or come in contact with it. In this paper, we propose algorithms for searching a rectangular region for a stationary hazardous entity, where some a priori geometrical knowledge is given (e.g., edge size range), and if such an entity exists, then determine the area that it occupies. We explore entities that are convex in nature such as line segment, circles (discs), and simple convex shapes. The objectives are to minimize the distance travelled by the robots during the search phase, and to minimize the number of robots that are required to identify the region covered by the hazardous entity. The number of robots required to locate H is three or four robots when H is a line segment, two or three robots when H is a circle, and seven robots are sufficient when H is a triangle. Our results extend to n-vertex convex shapes and we show that 2n + 1 robots are sufficient to determine the coverage of H.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".