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
The adjacent-vertex-distinguishing-total-colouring (AVD-total-colouring) problem was introduced and studied by Zhang et al. around 2005. This problem consists in associating colours to the vertices and edges of a graph G = (V (G), E(G)) using the least number of colours, such that: (i) any two adjacent vertices or adjacent edges receive distinct colours; (ii) each vertex receive a colour different from the colours of its incident edges; and (iii) for any two adjacent vertices u, v V (G), the set of colours that color u and its incident edges is distinct from the set of colours that color v and its incident edges. The smallest number of colours for which a graph G admits an AVD-total-colouring is named its AVDtotal chromatic number. Zhang et al. determined the AVD-total chromatic number for some classical families of graphs and noted that all of them admit an AVD-total-colouring with no more than (G) + 3 colours. Based on this observation, the authors conjectured that (G) + 3 colours would be sufficient to construct an AVD-total-colouring for any simple graph G. This conjecture is called the AVD-Total-Colouring Conjecture and remains open for arbitrary graphs, having been verified for a few families of 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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it