Defining the Range of Urban Congestion Impacts on Freight and Their Consequences for Business Activity
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
The causes and impacts of urban traffic congestion are intrinsically tied to changes occurring in business practices and the economy. The freight delivery requirements of businesses and their sensitivity to congestion are also increasing as many types of business seek to serve wider markets and apply new logistics and production technologies with increasing reliance on just-in-time supply chains, overnight courier services, intermodal facilities and international gateways. In response, regional business organizations are starting to take a leadership role in focusing attention on urban traffic congestion and its impacts on freight movement and business activity. This paper uses examples from three cases – Vancouver (BC), Chicago (IL) and Portland (OR) – to show how regional business organizations have been working with public agencies to study the economic implications of future congestion growth and the economic benefits of investing in efforts to mitigate it. It utilizes findings from those studies to develop a taxonomy of the many different ways in which urban traffic congestion is changing the freight delivery and operational decisions of businesses, and increasing their costs. It then identifies needs for improved transportation and economic analysis methods that are sensitive to those factors.
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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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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