Comparative Evaluation of Two Algorithms for Locating Contaminant Ingress Points
Bibliographic record
Abstract
A procedure involving Data Mining based on Flow Direction and shortest flow time (DMFD) is described, which consists of two components: (i) possible ingress nodes (PINs) identification and (ii) probability quantification. PINs identification is completed based on the flow information, i.e. the flow direction and time in each pipe. Through shortest time which is calculated by Dijksta algorithm, from one node to a specific sensor, ingress time in the node is obtained. A distance metric is described to quantify the probability of one node as PIN. A multi-stage response is described to decrease the elapsed time before a contaminant ingress event is identified and responded to, which is essential to minimize the risk from consumption of the hazard. The roles of two algorithms, namely a Data Mining method based on Injection and Detection information (DMID) and DMFD, are examined. A case study is employed in a network with 285 nodes and 5 sensors. The five sensors all alarm to the injection event. With DMID, the number of PINs is decreased from 44 in the 1st stage to 18, 11, 11 and 11 subsequently; For DMFD, the number is reduced from 44 in the 1st stage to 21, 21, 21 and 21 in the following stages; DMID identifies the true intrusion node 44 with the highest probability in the five stages, while DMFD identifies it with the highest probability in the 3rd and 4th stage; the run time of both DMFD and DMID is less than 2 min, which suggests the two are effective in guiding emergency response.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".