Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Consider a contaminated region in a 2-dimensional plane that is expanding with unit normal speed. Our goal is to apply a control function (e.g. pesticide) along the boundary of this region to reduce the contaminated area to zero in the shortest possible time, while consistently clearing a fixed amount of land per unit time. It has been proven that when the initial contamination forms a convex set, an optimal eradication strategy requires applying pesticide to the boundary segments with the highest curvature at each moment. This is equivalent to adopting a myopic strategy that minimizes the perimeter of the contaminated region at each time step. This result provided insight for developing numerical methods to compute approximations of these optimal strategies for convex sets. Namely, our simulation consists in alternating between computing the isoperimetric (perimeter-minimizing) profile with a convex constraint and then dilating the resulting set by a radius equal to the time mesh. This process is repeated until the shape reaches a circle, which can then be trivially scaled down to a point. The isoperimetric profile is obtained through morphological opening, an erosion-dilation operator commonly used in image processing for noise removal. A major open problem remains: a full characterization of optimal eradication strategies for initial contaminations of general shapes. We have applied the above numerical scheme with non-convex constraints to gain some intuition in this broader context. This may entail replacing the morphological opening operator with a more general algorithm relying on the medial axis of a shape. It turns out that, in some configurations, it is more effective to split the contamination into several components and shrink them separately.
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.
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 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 it