Computers, Environment andUrban Systems
Bibliographic record
Abstract
This paper presents a status report concerning on-going research anddevelopment work by a team of Canadian researchers to develop a microsimulation, agent-based, integrated model of urban land use and transportation. It describes in some detail the overall design and current status of the ILUTE (IntegratedLandUse, Transportation, Environment) modelling system under development. The overall purpose of ILUTE is to simulate the evolution of an entire urban region over an extended period of time. Such a model is intended to replace conventional, aggregate, static models for the analysis of a broadrange of transportation, housing andother urban policies. Agents being simulated in the model include individuals, households and establishments. The model operates on a ‘‘100 % sample’ ’ (i.e., the entire population) of agents which, in the base case, are synthesizedfrom more aggregate data such as census tables andwhich are then evolvedover time by the model. A range of modelling methods are employed within the modelling system to represent individual agents ’ behaviours, including simple state transition models, random utility choice models, rule-based ‘‘computational process’ ’ models, and hybrids of these approaches. A major emphasis within ILUTE is the development of microsimulation models of market demandsupply interactions, particularly within the residential and commercial real estate markets. In
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.021 | 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".