Testing microsimulation uncertainty of the parcel-based space development module of the Baltimore PECAS Demo Model
Bibliographic record
Abstract
A precise and stable microsimulation space development module is fundamental for supporting various policy decision-making exercises related to land development. This paper studies the dynamics or uncertainty of outputs of the parcel-based space development module of an integrated land-use and transport forecasting model—the Baltimore PECAS Demo Model. It is tested with two sub-studies: (1) running the model three times over the entire planning window from 2000 to 2030; and (2) running the model 30 times just one year ahead from 2000 to 2001. The outputs obtained are used to analyze such dynamics or uncertainty. Study results from the first sub-study show that, in general, the system is stable and consistent over runs and time, as supported by a set of paired t-tests. However, the coefficient of variation (COV) measuring the variation of estimated space quantity by category over four cross-section years indicates that the differences among runs are increasing over time through the planning window. The COV test over the second sub-study indicates the estimated space quantity is stable for most of the zones, except for a small portion of zones with a small space quantity.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 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".