Household Allocation Module of Oregon2 Model
Bibliographic record
Abstract
The Oregon2 model is a set of seven integrated modules that together simulate the land use-transport system in the state of Oregon. One of these modules, called the household allocation module, updates the economic and demographic attributes of each person and household in a synthetic population, including age, sex, occupation, work and student status, job holdings, household membership, primary- and secondary-home size and location, income, and car ownership. It uses an agent-based microsimulation with Monte Carlo selection to identify choice outcomes and state transitions for each person or household concerning each economic or demographic attribute considered, with selection probabilities determined by using logit models and sampling distributions that are functions of relevant alternative attributes and household or person characteristics. Initial parameter values were estimated with data from Oregon household travel surveys, the University of Michigan Panel Survey of Income Dynamics, U.S. census data, and other published statistics. Spatial locations are represented in a system of roughly 3,000 geographic zones covering the study area. Six housing types, eight occupation categories, and continuous quantities for housing size and income are also represented. Housing markets are represented by using a disequilibrium structure, in which prices in each zone are updated in response to the vacancy rate relative to a reference rate, moving toward but not necessarily reaching a market-clearing solution.
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.
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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.049 | 0.005 |
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".