Bibliographic record
Abstract
In this paper, we construct a Balanced Weekend Tournament, motivated by the real-life problem of scheduling an n-team double round-robin season schedule for a Canadian university soccer league. In this 6-team league, games are only played on Saturdays and Sundays, with the condition that no team has two road games on any weekend. The implemented regular-season schedule for n = 6 was best-possible, but failed to meet an important "compactness" criterion, as the 10-game tournament required more than five weekends to complete. The motivation for this paper was to determine whether an optimal season schedule, satisfying all of the league's constraints on compact balanced play, could be constructed for sports leagues with n > 6 teams. We present a simple recursive algorithm to answer this question for all even n > 6. As a corollary, our construction gives us an explicit solution to a challenging and well-known graph theory question, namely the problem of decomposing the complete directed graph K*2m into 2m–1 directed Hamiltonian cycles of length 2m.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".