Sensitivity Testing with the Oregon Statewide Integrated Model
Bibliographic record
Abstract
Oregon has a long history of developing and using integrated economic, land use–transport models. Development of the Oregon Statewide Integrated Model (SWIM) was commissioned by the Oregon Department of Transportation as part of its Transportation and Land Use Model Improvement Program within the larger Oregon Modeling Improvement Program. The first version model, now named SWIM1, has been used in numerous applications since the late 1990s. A more extensive second version, SWIM2, is now available. It uses the PECAS economic input–output activity allocation framework, an aggregate model of spatial development and microsimulation models of freight and person transport. This paper describes the work considered in the later stages of development of the SWIM2 model, including results of sensitivity testing and reports on concurrent actions to transfer the model to agency operation. The sensitivity tests considered three alternative scenarios covering the evolution of the statewide spatial economic and transport systems over a 19-year period. Each scenario was compared to a reference case. In one scenario, highway capacity was increased substantially along an Interstate corridor radiating out from the Portland metropolitan area for more than 100 mi. In the other two scenarios, the costs for vehicle travel were increased four- and 10-fold over 1998 costs. The model system was found to respond consistent with a priori expectations. Population and employment shift to areas of comparatively better accessibilities, urban densities change, trip lengths and modes change, and floor space development and prices respond to these changes in patterns that evolve across the state over time.
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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 | 0.007 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".