Optimized holistic municipal right-of-way capital improvement planning
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
Much of North America’s critical municipal right-of-way (ROW) infrastructure is facing a severe deficit in planned maintenance, rehabilitation, and renewal spending. An optimized holistic approach for capital improvement planning allows for the consideration of contiguity savings and efficiencies through the synchronization of rehabilitation and renewal projects for collocated segments from different ROW infrastructure component systems. This paper presents the results of the application of a holistic methodology to a small ROW network made up of segments of varying condition and criticality. This methodology was developed utilizing an evolutionary genetic algorithm to optimize a five-year capital improvement plan. The results from the application of the holistic model to an existing ROW network indicate that it is successful in achieving savings through synchronization and in providing superior maintenance, rehabilitation, and renewal plans when compared to the traditional paradigm where independent plans are created for road, sewer, and water utilities.
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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