The impact on environment of underground infrastructure utility work
Bibliographic record
Abstract
Canadian municipalities have noted that 59% of their water systems and 68% of their sewer systems required repair. To rehabilitate, replace or construct a water or sewer pipeline, several methods can be used: open-cut or one of several trenchless technologies (TTs). The selection of the method positively affects the surrounding environment. Therefore, the impact on environment (IoE) becomes a vital concern in selecting the appropriate construction or rehabilitation method for water or sewer pipelines. The research presented in this paper aims at developing an IoE model that compares the open-cut and TT methods. The IoE factors and their related data are collected, analysed and categorised based on literature review and expert opinion. Two models are designed in order to assess the IoE of the open-cut and TT methods. Results of the open-cut technique show that the impact on social factor has the maximum relative weight of 0.36; however, the impact on project characteristics has 0.33 and on environment has 0.30. The impact of TTs on project characteristics has the highest weight of 0.38, in which social factor is the lowest (0.28). The IoE value, using the open-cut method, is 0.4739; however, its value using the TT method is 0.3346. The developed IoE model shows robust results in quantifying the impact on environment of underground utility work. This research is relevant to both industry practitioners and researchers. It develops models to determine the IoE for the open-cut and TT methods. They are also beneficial to the municipal experts.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".