Bibliographic record
Abstract
Let $ϕ$ be a function that maps any non-empty subset $A$ of $\mathbb{R}^2$ to a non-empty subset $ϕ(A)$ of $\mathbb{R}^2$. A $ϕ$-cover of a set $T=\{T_1, T_2, \dots, T_m\}$ of pairwise non-crossing trees in the plane is a set of pairwise disjoint connected regions such that each tree $T_i$ is contained in some region of the cover, and each region of the cover is either (1) $ϕ(T_i)$ for some $i$, or (2) $ϕ(A \cup B)$, where $A$ and $B$ are constructed by either (1) or (2), and $A \cap B \neq \emptyset$. We present two properties for the function $ϕ$ that make the $ϕ$-cover well-defined. Examples for such functions $ϕ$ are the convex hull and the axis-aligned bounding box. For both of these functions $ϕ$, we show that the $ϕ$-cover can be computed in $O(n\log^2n)$ time, where $n$ is the total number of vertices of the trees in $T$.
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.
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 | 0.001 | 0.001 |
| 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 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".