Enumeration and asymptotic analysis of edge-disjoint triangle packings in complete graphs
Bibliographic record
Abstract
We investigate the combinatorial structure of edge-disjoint triangle packings in the complete graph \(K_n\). Two triangles are said to be edge-disjoint if they share no common edges, though they may share at most one vertex. For a given \(n\), let \(T_n\) denote the total number of subsets of triangles in \(K_n\) that are pairwise edge-disjoint, including the empty set, and let \(T_n^k\) denote the number of \(k\)-element sets of such triangles. In this article, we establish: (i) a general recurrence relation for \(T_n\) that enables asymptotic analysis, yielding the growth \(\log T_n = \Theta(n^2 \log n)\) for large \(n\); (ii) exact closed-form formulas for the number of edge-disjoint pairs (\(T_n^2\)), triples (\(T_n^3\)), and quadruples (\(T_n^4\)) of triangles in \(K_n\) for \(n \geq 6\). These results extend classical work on Steiner Triple Systems and provide new tools for analyzing triangle packings in complete graphs.
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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 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".