Comparisons from Sacramento Model Test Bed
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
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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