Using Qualitative Indicators in Infrastructure Assessment Using the Functionality–Resiliency–Sustainability Framework
Bibliographic record
Abstract
This paper presents the implementation of decision criteria template to incorporate qualitative indicators in functionality, resiliency and sustainability assessments of stormwater infrastructure using the Functionality–Resiliency–Sustainability Framework. Qualitative attributes, even though deemed important, are usually not included, or adequately considered because of their inherent subjectivity associated with assessing them. Whenever qualitative indicators are used in practice, an ordinal scale such as poor, fair, good, very good is frequently applied. However, depending on the understanding and priorities of the evaluator, the meaning of poor, fair, good and very good score can differ significantly. Such indicators were generally not intended to facilitate engineering solutions but rather management related concerns. However, understanding the issues behind using qualitative measures is critical for stormwater infrastructure because many non-conventional indicators—which are frequently non-technical measures—can be used to assess the resiliency and sustainability of stormwater system. Moreover, there are few resources to adapt specific qualitative factors to quantitative equivalents. To address this gap, a decision criteria template is developed to score qualitative indicators within a multi-criteria assessment approach so that consistency, transparency, and subjectivity in the assessment can be addressed. The indicator scoring template is illustrated in an example stormwater system in city of Windsor, Ontario, Canada. Based on the analysis done using the decision criteria matrix, it was found that city of Windsor's stormwater infrastructure resiliency is relatively better than its assessed functionality and sustainability.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".