GIS-inventory and condition rating of water supply system at McGill Downtown Campus
Bibliographic record
Abstract
The research project presents a framework for developing a detailed Geographic Information System (GIS) inventory and condition rating of the water supply system at McGill Downtown Campus (MDC), based on the previous work by Sipos (2006), and Sipos and Mirza (2008). The GIS, consisting of a comprehensive geo-referenced base-map with the water supply system and other infrastructure features at MDC, was developed employing a Bentley software platform. Several non-destructive evaluation (NDE) tests were performed for detection, location and condition assessment of the water supply system at MDC. Based on the data edited in the GIS, including historical engineering data and the data acquired from in-situ NDE testing, an algorithm was developed for condition rating of the water supply pipes, incorporating 31 parameters, which can possibly contribute to the deterioration of the water pipes, and influence their service lives adversely. The rating scores provided by the algorithm were incorporated in the base map by implementing a color-coded system. An application was also developed, aimed at displaying a calculation summary of the rating score for the water pipes directly on the base-map. The project also discusses the current state of the water supply infrastructure in Canada, the related problems and needs. The various NDE methodologies which can be successfully employed for detection and condition assessment of the water supply systems are presented, along with several recommendations for use. The details for developing a schema for inventory and condition assessment, and the quantification of the influence of each parameter considered in the rating algorithm, are also provided. Condition rating of the water supply pipes at MDC is performed by implementing the proposed framework and the analysis results are discussed. The conclusions and the recommendations and the future research needs are proposed. The original contributions to the field of knowledge are also summarized. The framework is aimed at developing a powerful tool for management, operation and prioritization for repair / rehabilitation / replacement of the water supply systems in any municipality in Canada, and other infrastructure systems, provided the framework is adapted for the specific characteristics of the different infrastructure systems and the environmental, operation and other varying local conditions.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".