Column planarity and partially-simultaneous geometric embedding
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Bibliographic record
Abstract
We introduce the notion of column planarity of a subset $R$ of the vertices of a graph $G$. Informally, we say that $R$ is column planar in $G$ if we can assign $x$-coordinates to the vertices in $R$ such that any assignment of $y$-coordinates to them produces a partial embedding that can be completed to a plane straight-line drawing of $G$. Column planarity is both a relaxation and a strengthening of unlabeled level planarity. We prove near tight bounds for the maximum size of column planar subsets of trees: every tree on $n$ vertices contains a column planar set of size at least $14n/17$ and for any $\epsilon > 0$ and any sufficiently large $n$, there exists an $n$-vertex tree in which every column planar subset has size at most $(5/6 + \epsilon)n$. In addition, we show that every outerplanar graph has a column planar set of size at least $n/2$. We also consider a relaxation of simultaneous geometric embedding (SGE), which we call partially-simultaneous geometric embedding (PSGE). A PSGE of two graphs $G_1$ and $G_2$ allows some of their vertices to map to two different points in the plane. We show how to use column planar subsets to construct $k$-PSGEs, which are PSGEs in which at least $k$ vertices are mapped to the same point for both graphs. In particular, we show that every two trees on $n$ vertices admit an $11n/17$-PSGE and every two outerplanar graphs admit an $n/4$-PSGE.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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 it