Giving Utility Companies a Seat at the Table: How Austin Water Is Addressing Utility Conflicts and Relocations for Project Connect
Bibliographic record
Abstract
A large part of a city’s growth is major transportation and transit projects. These major transit projects are a once in a generation opportunity to move people and vehicles on a large scale. In the fall of 2020, the citizens of Austin passed a $7.1 billion transit plan called Project Connect, a comprehensive transit plan aimed at enhancing public transportation in Austin, Texas. It is comprised of a new light rail transit (LRT) system, an expanded bus system, and a transition to an all-electric vehicle fleet. The Austin Transit Partnership (ATP), an independent local government organization formed by the City of Austin and CapMetro, manages the Project Connect investment, including design and construction of the new transportation infrastructure. Coordination is ongoing between ATP, CapMetro, Austin Water (AW), Austin Energy, Watershed Protection, numerous planning and design consultants, and the public for implementation of Project Connect. The Pape-Dawson Team (PD) was selected and contracted by the City to evaluate specific major utility conflicts identified by AW staff dedicated to this project. The AW Team has been working independently from the Project Connect Team responsible for the light rail improvements. Typically, large transit focused projects do not consult utility companies until design has significantly progressed. By getting ahead of the game, AW has been able to evaluate alternative alignments for their proposed pipeline assets and relocation of their existing infrastructure without the constraints of a transit design that is already set. This approach allows AW to provide alternatives that meet their best interest for serving their customers and future operations and maintenance (O&M).
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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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.014 | 0.004 |
| Scholarly communication | 0.020 | 0.011 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.078 | 0.014 |
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".