Washington State All-Weather Road GIS Mapping: Improving Statewide Freight Flows and Connectivity
Bibliographic record
Abstract
Freight transportation is extremely important in Washington as it underpins the national and state economies, supports national defense and facilitates the distribution of goods and services to nearly all state residents on a daily basis. Because of this, the freight transportation system in Washington must function at all levels of government from international, national, to state and local. Internationally, Washington is a “gateway” state connecting Asian & Canadian trade flows to the U.S. economy. Nationally, Washington's freight system facilitates trade from Alaska and along the west coast. Additionally, the state's firms and farmers use the freight transportation system to ship Washington made goods locally, across the country and around the world. And lastly, Washington's freight transportation system serves as a local utility, vital to citizens throughout the state to meet their consumption needs. As a result, the planning of freight improvement projects should be seamless across various government jurisdictions and county/state boundaries. To understand the need for the coordinated collection and presentation of data, one needs only consider the various types of roads that contribute to the freight transportation system in Washington. Not only does the state host international border crossings and interstate highways, it is comprised of a myriad of county and city roads. As a result, there are several key agencies that have a stake in freight transportation planning in Washington: Washington State Department of Transportation (WSDOT), County Road Administration Board (CRAB), Strategic Freight Transportation Analysis (SFTA) as well as the county and municipal governments. In the process of completing this study, it was necessary to contact each of these agencies for assistance in the collection of data and maps. To assist planners and policymakers at all levels of government, this paper describes the statistical and geospatial data collected from each county in Washington State and the analytical application of a complete GIS mapping of all-weather roads throughout the state, along with future county freight improvement projects. This information is then evaluated and analyzed on a regional/statewide basis to identify gaps or system inefficiencies resulting from local/county improvement projects that don’t extend across county borders. Specific county and regional case studies/examples of how this centralized GIS may aid policy decision-maker are also presented.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".