Using CFS Data to Guide Regional Transportation Policy and Investment
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
Commodity Flow Survey (CFS) data has played a significant role to help set the context for regional transportation policy and investment decisions in the Portland–Vancouver region. Data from the CFS has been a primary input into the region’s Commodity Flow Forecast (1997) and the update in 2002. The CFS has also provided data that has helped answer business community questions about freight flows and engaged them in policy discussions regarding the Columbia River crossing as part of the Interstate 5 Trade Corridor project. Both directly and indirectly, the CFS has been very helpful in helping us set the context for freight movement and to put freight issues on the regional transportation agenda. CFS data gives us the ability to frame the issues, convey the order of magnitude of freight’s importance, and to identify areas where further data is needed. Ultimately, we would like to be able to use the data at a project level, but the CFS doesn’t provide enough detail. That is to say, we would like to have the data at detail level sufficient to help make the case for a specific investment or to prioritize among competing investments. However, even at current levels of detail, the CFS has been useful. Due in part to CFS data in our Commodity Flow Forecast, we have secured $500,000 in regional funding for a freight data collection project that will provide us with some of the detail we need to make specific investment decisions, such as origin–destination and time of day data. This presentation showed how and why our region has successfully used CFS data, identified where we have found gaps and problems, and suggested alternatives for making CFS data more accessible and more useful at a regional level.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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