But Why There? A Data-Driven Approach to Route Selection
Bibliographic record
Abstract
Do utilities know the best place to put new pipeline infrastructure? We think so. But does the data support those decisions? How can we demonstrate to the public that we are doing our due diligence? How do we show that the process is more than qualitative considerations? What if the pipeline is nearly 30 mi long and 66-in. in diameter, winding through protected wetlands, 10-lane interstate highways, dense residential neighborhoods, and established commercial business areas? What about estimated construction costs up to half a billion dollars? Tampa Bay Water and Hillsborough County (HC) were faced with these exact questions as they looked to finalize an alignment for critical future pipeline infrastructure. Pipeline route evaluation and selection is not a one-size-fits-all approach. It is usually tailored to the project scope and funding available, accordingly scaling up or down, to align with the end needs of the project stakeholders. For this project, the non-cost evaluation process was broken into the following key steps: (1) screening of a universe of options to a manageable shortlist; (2) a stakeholder-driven weighting criteria pairwise exercise to determine relative importance of key evaluation criteria; (3) data collection and visualization: using a “real-time,” accessible, online GIS platform to establish preliminary route corridors and set baseline evaluation metrics to “evaluate” various routes; (4) selection and development of sub-criteria to provide more discreetly definable and measurable evaluation characteristics and quantitatively measuring seemingly intangible evaluation factors such as public inconvenience; and (5) a quantitative methodology to integrate non-cost criteria (unitless) and cost (dollars). Simply adding the two does not work, as the units are incompatible. With this groundwork completed, the once-complicated decision becomes an effort of using the data to drive decisions.
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.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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".