Diagnosing Faulty Cable Network Segments From Modem Power Readings
Bibliographic record
Abstract
This paper describes our investigation of the possible use of modem status data to discover faulty behavior in a cable network. Early analysis of the data revealed that the modem power signal had a daily variation which was congruent with the diurnal temperature variation. Based on this observation, we hypothesized early on that variations from this daily cycle could potentially indicate an abnormal (faulty) behavior. We developed specific metrics to quantify the magnitude of the aberration on a per modem basis. We have used this metric to develop a segment interest metric applicable to the segment level. We conclude that the proposed segment power interest metric derived solely from status information obtained from the installed modem base, was proven to be implied by a corresponding WSR interest metric. This is an important outcome, as it establishes the segment power interest metric to be a strong candidate to be used as predictor of the "health" of the corresponding segment.
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How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".