On <span class="math inline">\(z\)</span>-cycle factorizations with two associate classes where <span class="math inline">\(z\)</span> is in <span class="math inline">\(\{4,4a\}\)</span> with even parameters
Bibliographic record
Abstract
Let \(K = K(a,p;\lambda_1,\lambda_2)\) be the multigraph with: the number of vertices in each part equal to \(a\); the number of parts equal to \(p\); the number of edges joining any two vertices of the same part equal to \(\lambda_1\); and the number of edges joining any two vertices of different parts equal to \(\lambda_2\). The existence of \(C_4\)-factorizations of \(K\) has been settled when \(a\) is even; when \(a \equiv 1 \ (\mbox{mod } 4)\) with one exception; and for very few cases when \(a \equiv 3 \ (\mbox{mod } 4)\). The existence of \(C_z\)-factorizations of \(K\) has been settled when \(a \equiv 1 \ (\mbox{mod } z)\) and \(\lambda_1\) is even; when \(a \equiv 0 \ (\mbox{mod } z)\); and when \(z=2a\) where both \(a\) and \(\lambda_1\) is even. In this paper, we give a construction for \(C_z\)-factorizations of \(K\) for \(z \in \{ 4,4a \}\) when \(a\) is even.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.124 | 0.048 |
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".