The <i>g</i> -extra diagnosability of the generalized exchanged hypercube
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
Diagnosability of a self-diagnosable interconnection structure specifies the maximum number of faulty vertices such a structure can identify by itself. A variety of diagnosability models have been suggested. It turns out that a diagnosability property of a network structure is closely associated with its relevant connectivity property. Based on this observation, a general diagnosability derivation process has been suggested. The g-extra connectivity of a graph G characterizes the size of a minimum vertex set F such that, when it is removed, every component in the disconnected survival graph, G−F, contains at least g + 1 vertices. In this paper, we discuss the aforementioned general derivation process, derive the g-extra connectivity, and then apply the aforementioned general process to reveal the g-extra diagnosability of the generalized exchanged hypercube.
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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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.005 | 0.001 |
| 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