An ArcGIS Tool for Rapid Contaminant Source Identification
Bibliographic record
Abstract
The contaminant source identification (CSI) problem consists of five parts: i) identification of possible intrusion nodes (PINs), ii) quantification of the probability of each PIN as the true intrusion node, iii) identification of priority nodes (PNs) from PINs which are upstream of important nodes such as schools, hospitals, and multi-story buildings, iv) quantification of the priority degree of each PN, which indicates importance for emergency response, and iv) identifying whether a PIN connects aging pipe(s) which possess high potential for contaminant intrusion. The locations of PINs, PNs, and the connection of aging pipe(s), require involvement of geographic information system (GIS). An ArcGIS tool namely GIS-CSI is developed to integrate various data sources into ArcGIS feature class, and implement the CSI algorithm developed in Shen et al. (2009b) to solve the CSI problem, and to display the CSI outputs, i.e., PINs and PNs locations, their probabilities as intrusion nodes and priority degree respectively. In the tool demonstration, immediately after each sensor alarm, GIS-CSI can run the CSI algorithm within 5 min, and export the outputs to the water distribution system map to greatly facilitate emergency response.
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.
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".