Geospatial Visual Analytics for Supporting Decision Making for Underground Utility Integrated Interventions
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
When considering a holistic approach to infrastructure asset management, insights can be drawn from the visualization and interpretation of the multivariate data sets that capture the need for integrated interventions. The decision-making process associated with synchronized, integrated asset management can benefit from data fusion analytics. Previously, visual analytics has been applied to understand the processes associated with individual infrastructure assets such as bridges, pavements, sewers, etc. This research proposes using geospatial visual analytics to interpret and visualize an inventory of data collected from several sources, including annual average daily traffic (AADT), intervention plan, emergency repair data, water pipe networks, and excavation data. The aim is to analyze and determine the relationships between attributes of the data sets using statistical tools and geospatial analysis. Analyzing these relationships improves coordinated utility maintenance and repair, provides insight into the socio-economic impact of these activities, and helps select intervention alternatives while optimizing the costs.
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.
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) | 0.001 | 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 it