The Minimum Consistent Spanning Subset Problem on Trees
Bibliographic record
Abstract
Given a vertex-colored edge-weighted graph, the minimum consistent subset (MCS) problem asks for a minimum subset $S$ of vertices such that every vertex $v\notin S$ has the same color as its nearest neighbor in $S$. This problem is NP-complete. A recent result of Dey, Maheshwari, and Nandy (2021) gives a polynomial-time algorithm for the MCS problem on two-colored trees. A block is a maximal connected set of vertices of the same color. We introduce a variant of the MCS problem, namely the minimum consistent spanning subset problem, for which we require the set $S$ to contain a vertex from every block of the graph such that every vertex $v\notin S$ has a nearest neighbor in $S$ that is in the same block as $v$. We observe that this problem is NP-hard on general graphs. We present a polynomial-time algorithm for this problem on trees.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 source (direct Gemma or distilled Codex), 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".