Guarding Galleries with No Nooks (Extended Abstract)
Bibliographic record
Abstract
) David Kirkpatrick kirk@cs.ubc.ca Department of Computer Science University of British Columbia Vancouver BC, Canada V6T 1Z4 July 4, 2000 Abstract We consider the problem of guarding galleries that have no small nooks (regions that are visible from only a small fraction of the entire gallery). Intuitively, such galleries (of which convex galleries are a special but uninteresting case) should need fewer guards. We show that in any simply connected gallery in which every corner sees at least a fraction ffl of the other corners there exist a set of at most 64 1 ffl log log 1 ffl corners (guard stations) from which every other corner is visible. Similar results also hold for guarding the entire boundary with guards stationed at points on the boundary. These results are similar but slightly stronger than analogous results known for guarding the interior of a gallery. We provide a direct and simple guard assignment scheme that realizes the size bounds. 1 Introduction We consi...
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.019 | 0.004 |
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".