A Bayesian Network approach to diagnosing the root cause of failure from Trouble Tickets
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
Telecommunications networks comprise elements of very different types that work together to provide services. Quite often, hardware failures are interrelated and it is hard for technicians specialized in specific hardware to find out these relationships. In this context, Bayesian Networks (BN) provide a good and flexible solution because they allow us to model the causal relationships between element failures and infer information from existing evidence. The goal is that network technicians can be informed of the real scope of failures and the probable existence of root problems, thus optimizing resources and reducing recovery time. Besides, with this approach a real element hierarchy can be built, allowing the discovery of hidden dependencies between elements. The outcome of this work has been the development of a rooting module attached to an incident management system (trouble ticketing system, TT).
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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 | 0.005 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 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