Decontaminating planar regions by sweeping with barrier curves
Bibliographic record
Abstract
\If seven maids with seven mops Swept it for half a year. Do you suppose, the Walrus said, \That they could get it clear? \I doubt it, said the Carpenter, And shed a bitter tear. We consider the problem of decontaminating (cleaning) the interior of a planar shape by sweeping it with barrier curves. The contaminant is assumed to instantly travel any path not blocked by a barrier. We show that any decontamination sweep can be converted to one that uses only line segment barriers without increasing length. We dene the sweepwidth of a region as the minimum over all decontamination sweeps of the maximum over time of barrier length used, and determine sweepwidth for some simple classes of orthogonal polygons. However, we also show that computing sweepwidth in general, even for orthogonal polygons, isNP-hard.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".