The combinatorial game N<scp>ofil</scp> played on Steiner triple systems
Bibliographic record
Abstract
Abstract We introduce an impartial combinatorial game on Steiner triple systems called Next One to Fill Is the Loser (N ofil) . Players move alternately, choosing points of the triple system. If a player is forced to fill a block on their turn, they lose. By computing nim‐values, we determine optimal strategies for N ofil on all Steiner triple systems up to order 15 and a sampling for orders 19, 21 and 25. The game N ofil can be thought of in terms of play on a corresponding hypergraph which will become a graph during play. At that point N ofil is equivalent to playing the game N ode K ayles on the graph. We prove necessary conditions and sufficient conditions for a graph to reached playing N ofil. We conclude that the complexity of determining the outcome of the game N ofil on Steiner triple systems is PSPACE‐complete for randomized reductions.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".