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
Given n lines in the plane, a (1/r)-cutting is a subdivision of the plane into cells such that each cell intersects at most n/r lines. Cuttings are fundamental to the design of geometric divide-and-conquer algorithms and have numerous applications. Early suboptimal constructions of cuttings were given implicitly in the works by Megiddo and by Dyer in the 80s; simple randomized constructions were later discovered by Clarkson and by Haussler and Welzl; subsequently deterministic algorithms were given by Chazelle and Friedman, by Matousek, and by Agarwal; eventually O(nr)-time deterministic algorithms to construct (1/r)-cuttings of optimal O(r) size were obtained by Matousek and by Chazelle in the early 90s. In this talk, I will survey some of these past works. I will also give a self-contained presentation of an O(nr)-time deterministic algorithm in 2D which does not require any background on derandomization techniques and which (I hope) is easy to understand.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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