Full friendly index sets and full product-cordial index sets of some permutation petersen graphs
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
LetG = (V,E) be a connected graph without loops. A vertex labeling g : V [arrow right] Z^sub 2^ induces two edge labelings f^sup +^, f* : E [arrow right] Z^sub 2^, given by f^sup +^(uv) = f(u) + f(v) and f*(uv) = f(u)f(v) for each uv ∈ E respectively. For j ∈ Z^sub 2^, let v^sub f^ (j) = |f^sup -1^(j)|, e^sub f+^(j) = |(f^sup +^)^sup -1^(j)| and e^sub f*^ (j) = |(f*)^sup -1^(j)|. A vertex labeling f is called friendly if |v^sub f^ (1) - v^sub f^ (0)| ≤ 1. For a friendly labeling f of G, the friendly index of G with respect to f is defined to be i^sup +^^sub f^ (G) = e^sup +^^sub f+^(1) - e^sub f+^(0), and the product-cordial index is defined to be i*^sub f^ (G) = e^sub f*^(1) - e^sub f*^(0). The full friendly index set (FFI) and the full product-cordial index set (FPCI) of G contain precisely all the values i^sup +^^sub f^ (G) and i*^sub f^ (G) taken over all friendly labelings of G, respectively. In this paper, we study the FFI and the FPCI of odd twisted cylinder and two permutation Petersen graphs.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it