The allow sequence of distinct eigenvalues for a sign pattern
Bibliographic record
Abstract
A sign pattern $\mathcal{A}$ is a matrix with entries in $\{+,-,0\}$. This article introduces the allow sequence of distinct eigenvalues for an $n\times n$ sign pattern $\mathcal{A}$, defined as $q_{\rm seq}(\mathcal{A})=\langle q_1,\ldots,q_n\rangle$, with $q_k=1$ if there exists a real matrix with exactly $k$ distinct eigenvalues having pattern $\mathcal{A}$, and $q_k=0$ otherwise. For example, $q_{\rm seq}(\mathcal{A})=\langle 0,\ldots,0,1\rangle$ is equivalent to $\mathcal{A}$ requiring all distinct eigenvalues, while $q_{\rm seq}(\mathcal{A})=\langle 1,0,\ldots,0\rangle$ is equivalent to the digraph of $\mathcal{A}$ being acyclic. Relationships between the allow sequence for $\mathcal{A}$ and composite cycles of the digraph of $\mathcal{A}$ are explored to identify zeros in the sequence, while methods based on Jacobian matrices are developed to identify ones in the sequence. When $\mathcal{A}$ is an $n\times n$ irreducible sign pattern, the possible sequences for $q_{\rm seq}(\mathcal{A})$ are completely determined when $n\leq 4$ and when the sequence has at least $n-4$ trailing zeros for $n\geq 5$.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.025 | 0.005 |
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".