Comparisons from Sacramento Model Test Bed
Bibliographic record
Abstract
Three land use and transport interaction models were applied to the Sacramento, California, region by various teams of researchers. The results of these efforts were compared with each other and with the traditional transport demand model used by the regional government. The results of the modeling efforts are compared, with the focus being on how the design of the modeling frameworks and their application influenced the modeling results. A trend scenario was compared with three different policy scenarios: one that involved high-occupancy vehicle (HOV) lane construction, one that added beltway construction as well as HOV construction, and a third that involved light rail construction and limited pricing of automobile use. The results differ among the different models for the trend scenario, as well as for each model with respect to scenario-to-trend comparisons. The results show some of the limitations of aggregate models calibrated to cross-sectional data. The differences between the models provide important insight into how models should be calibrated and how their results should be used. Uncertainty in land use transport interaction models seems inevitable, and further research should investigate how such modeling frameworks should best be used to understand the influence of policy in the face of uncertain futures.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 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".