Markov chain modulated Poisson process to stimulate the number of blockages in sewer networks
Bibliographic record
Abstract
Blockage failure is the most common type of operational failure in sewer networks that can cause loss of service and flooding, which can result in environmental pollution, health risks, property damage, and traffic disruption. There are currently very few blockage prediction models that are either deterministic in nature or depend only on static factors in predicting blockages. This study aims to overcome these drawbacks in the current blockage prediction models and proposes a methodology that aims to predict the expected number of blockages in sewer networks as a function of pipe conditions (dynamic variable) and pipe physical attributes (static variables) using a Markov chain modulated Poisson process modeling framework. The framework is applied to case study sewer network in the city of Sahab, Jordan, that contains information about the pipes’ physical attributes and their current condition. Bayesian analysis is then performed to evaluate the proposed framework.
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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".