Federal Highway Administration Scenario Planning Peer Workshop: Vancouver, Washington April 28, 2011
Bibliographic record
Abstract
This report summarizes key findings from a one-day scenario planning workshop held in Vancouver, Washington. The Federal Highway Administration (FHWA), the Southwest Washington Regional Transportation Council (RTC), and the Washington Department of Transportation (WSDOT) jointly sponsored and hosted the workshop. RTC is the metropolitan planning organization (MPO) for the Vancouver and Portland (Oregon) urbanized area and is the state-designated Regional Transportation Planning Organization for Clark, Skamania, and Klickitat counties in Washington. Clark County has experienced extensive population growth over the past twenty years. The county is expected to add nearly 220,000 people by 2035; however, the Cascade Mountains and two rivers bordering the county leave little room for expansion. RTC aims to use scenario planning as a mechanism to help determine effective land use and transportation plans that can accommodate Clark County’s projected population growth. The goals of the workshop were to: • Provide RTC staff, local elected officials, and Clark County community leaders with an overview of scenario planning and potential process steps; • Share notable examples of how agencies around the country have successfully applied scenario planning; • Demonstrate the benefits of scenario planning; and • Brainstorm potential initial steps for applying scenario planning to Clark County and key resources that might be needed for this effort. \n
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.176 | 0.051 |
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".