A new probing scheme for fault detection and identification
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
Probing technology has been used as a fault detection and identification method in computer networks and successful applications have been reported. One of the most appealing features of probing-based schemes is that it is an active approach. A set of probes can be sent on a periodic basis. If a network failure is detected, the outcomes of these probes are further analyzed to determine the root cause of the problem. However, the availability of a large set of such probes may in fact place a huge burden on management systems in terms of extra management traffic and storage space. Hence, the need of minimizing such a probing set has become highly desirable. In this work, we propose a preplanned probe selection scheme, in which a small set of probes are chosen such that it maintains the diagnostic power of the original set. The new approach is based on the constraint satisfaction problem paradigm and its powerful search techniques are exploited. The efficiency of the new algorithm has been demonstrated by the results reported.
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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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